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### Tips for Accelerating MATLAB Performance

latest technical articles - Thu, 06/15/2017 - 05:30
An independent MATLAB consultant shows how, by investing a small amount of time, you can accelerate MATLAB programs by a large factor.

### Fitting and Extrapolating U.S. Census Data

latest technical articles - Wed, 06/14/2017 - 05:30
This article shows how to extrapolate the size of the U.S. population using polynomials, splines, exponentials, and the census app in Cleve’s Laboratory.

### Hilbert Matrices

latest Blogs - Thu, 06/08/2017 - 02:25

I first encountered the Hilbert matrix when I was doing individual studies under Professor John Todd at Caltech in 1960. It has been part of my professional life ever since.

ContentsDavid Hilbert

Around the turn of the 20th century, David Hilbert was the world's most famous mathematician. He introduced the matrix that now bears his name in a paper in 1895. The elements of the matrix, which are reciprocals of consecutive positive integers, are constant along the antidiagonals.

$$h_{i,j} = \frac{1}{i+j-1}, \ \ i,j = 1:n$$

format rat H5 = hilb(5) H5 = 1 1/2 1/3 1/4 1/5 1/2 1/3 1/4 1/5 1/6 1/3 1/4 1/5 1/6 1/7 1/4 1/5 1/6 1/7 1/8 1/5 1/6 1/7 1/8 1/9 latex(sym(H5));

$$H_5 = \left(\begin{array}{ccccc} 1 & \frac{1}{2} & \frac{1}{3} & \frac{1}{4} & \frac{1}{5}\\ \frac{1}{2} & \frac{1}{3} & \frac{1}{4} & \frac{1}{5} & \frac{1}{6}\\ \frac{1}{3} & \frac{1}{4} & \frac{1}{5} & \frac{1}{6} & \frac{1}{7}\\ \frac{1}{4} & \frac{1}{5} & \frac{1}{6} & \frac{1}{7} & \frac{1}{8}\\ \frac{1}{5} & \frac{1}{6} & \frac{1}{7} & \frac{1}{8} & \frac{1}{9} \end{array}\right)$$

Here's a picture. The continuous surface generated is very smooth.

H12 = hilb(12); surf(log(H12)) view(60,60) Least squares

Hilbert was interested in this matrix because it comes up in the least squares approximation of a continuous function on the unit interval by polynomials, expressed in the conventional basis as linear combinations of monomials.

$$p(x) = \sum_{j=1}^n c_j x^{j-1}$$

The coefficient matrix for the normal equations has elements

$$\int_0^1 x^{i+j-2} dx \ = \ \frac{1}{i+j-1}$$

Properties

A Hilbert matrix has many useful properties.

• Symmetric.
• Positive definite.
• Hankel, $a_{i,j}$ is a function of $i+j$.
• Cauchy, $a_{i,j} = 1/(x_i - y_j)$.
• Nearly singular.

Each column of a Hilbert matrix is nearly a multiple of the other columns. So the columns are nearly linearly dependent and the matrix is close to, but not exactly, singular.

hilb

MATLAB has always had functions hilb and invhilb that compute the Hilbert matrix and its inverse. The body of hilb is now only two lines.

J = 1:n; H = 1./(J'+J-1);

We often cite this as a good example of singleton expansion. A column vector is added to a row vector to produce a matrix, then a scalar is subtracted from that matrix, and finally the reciprocals of the elements produce the result.

Inverse

It is possible to express the elements of the inverse of the Hilbert matrix in terms of binomial coefficients. For reasons that I've now forgotten, I always use $T$ for $H^{-1}$.

$$t_{i,j} = (-1)^{i+j} (i+j-1) {{n+i-1} \choose {n-j}} {{n+j-1} \choose {n-i}} {{i+j-2} \choose {i-1}}^2$$

The elements of the inverse Hilbert matrix are integers, but they are large integers. The largest ones are on the diagonal. For order 13, the largest element is

$$(T_{13})_{9,9} \ = \ 100863567447142500$$

This is over $10^{17}$ and is larger than double precision flintmax.

format longe flintmax = flintmax flintmax = 9.007199254740992e+15

So, it is possible to represent the largest elements exactly only if $n \le 12$.

The HELP entry for invhilb includes a sentence inherited from my original Fortran MATLAB.

The result is exact for N less than about 15.

Now that's misleading. It should say

The result is exact for N <= 12.

(I'm filing a bug report.)

invhilv

The body of invhilb begins by setting p to the order n. The doubly nested for loops then evaluate the binomial coefficient formula recursively, avoiding unnecessary integer overflow.

dbtype invhilb 18:28 18 p = n; 19 for i = 1:n 20 r = p*p; 21 H(i,i) = r/(2*i-1); 22 for j = i+1:n 23 r = -((n-j+1)*r*(n+j-1))/(j-1)^2; 24 H(i,j) = r/(i+j-1); 25 H(j,i) = r/(i+j-1); 26 end 27 p = ((n-i)*p*(n+i))/(i^2); 28 end

I first programmed this algorithm in machine language for the Burroughs 205 Datatron at Caltech almost 60 years ago. I was barely out of my teens.

Here's the result for n = 6.

format short T6 = invhilb(6) T6 = 36 -630 3360 -7560 7560 -2772 -630 14700 -88200 211680 -220500 83160 3360 -88200 564480 -1411200 1512000 -582120 -7560 211680 -1411200 3628800 -3969000 1552320 7560 -220500 1512000 -3969000 4410000 -1746360 -2772 83160 -582120 1552320 -1746360 698544

A checkerboard sign pattern with large integers in the inverse cancels the smooth surface of the Hilbert matrix itself.

T12 = invhilb(12); spy(T12 > 0)

A log scale mitigates the jaggedness.

surf(sign(T12).*log(abs(T12))) view(60,60) Rookie experiment

Now using MATLAB, I am going to repeat the experiment that I did on the Burroughs 205 when I was still a rookie. I had just written my first program that used Gaussian elimination to invert matrices. I proceeded to test it by inverting Hilbert matrices and comparing the results with the exact inverses. (Today's code uses this utility function that picks out the largest element in a matrix.)

maxabs = @(X) max(double(abs(X(:))));

Here is n = 10.

n = 10 H = hilb(n); X = inv(H); % Computed inverse T = invhilb(n); % Theoretical inverse E = X - T; % The error err = maxabs(E) n = 10 err = 5.0259e+08

At first I might have said:

Wow! The error is $10^8$. That's a pretty big error. Can I trust my matrix inversion code? What went wrong?

But I knew the elements of the inverse are huge. We should be looking at relative error.

relerr = maxabs(E)/maxabs(T) relerr = 1.4439e-04

OK. The relative error is $10^{-4}$. That still seems like a lot.

I knew that the Hilbert matrix is nearly singular. That's why I was using it. John Todd was one of the first people to write about condition numbers. An error estimate that involves nearness to singularity and the floating point accuracy would be expressed today by

esterr = cond(H)*eps esterr = 0.0036

That was about all I understood at the time. The roundoff error in the inversion process is magnified by the condition number of the matrix. And, the error I observe is less than the estimate that this simple analysis provides. So my inversion code passes this test.

Deeper explanation

I met Jim Wilkinson a few years later and came to realize that there is more to the story. I'm not actually inverting the Hilbert matrix. There are roundoff errors involved in computing H even before it is passed to the inversion routine.

Today, the Symbolic Math Toolbox helps provide a deeper explanation. The 'f' flag on the sym constructor says to convert double precision floating point arguments exactly to their rational representation. Here's how it works in this situation. To save space, I'll print just the first column.

H = hilb(n); F = sym(H,'f'); F(:,1) ans = 1 1/2 6004799503160661/18014398509481984 1/4 3602879701896397/18014398509481984 6004799503160661/36028797018963968 2573485501354569/18014398509481984 1/8 2001599834386887/18014398509481984 3602879701896397/36028797018963968

The elements of H that are not exact binary fractions become ratios of large integers. The denominators are powers of two; in this case $2^{54}$ and $2^{55}$. The numerators are these denominators divided by $3$, $5$, etc. and then rounded to the nearest integer. The elements of F are as close to the exact elements of a Hilbert matrix as we can get in binary floating point.

Let's invert F, using the exact rational arithmetic provided by the Symbolic Toolbox. (I couldn't do this in 1960.)

S = inv(F);

We now have three inverse Hilbert matrices, X, S, and T.

• X is the approximate inverse computed with floating point arithmetic by the routine I was testing years ago, or by MATLAB inv function today.
• S is the exact inverse of the floating point matrix that was actually passed to the inversion routine.
• T is the exact Hilbert inverse, obtained from the binomial coefficient formula.

Let's print the first columns alongside each other.

fprintf('%12s %16s %15s\n','X','S','T') fprintf('%16.4f %16.4f %12.0f\n',[X(:,1) S(:,1) T(:,1)]') X S T 99.9961 99.9976 100 -4949.6667 -4949.7926 -4950 79192.8929 79195.5727 79200 -600535.3362 -600559.6914 -600600 2522211.3665 2522327.5182 2522520 -6305451.1288 -6305770.4041 -6306300 9608206.6797 9608730.4926 9609600 -8750253.0592 -8750759.2546 -8751600 4375092.6697 4375358.4162 4375800 -923624.4113 -923682.8529 -923780

It looks like X is closer to S than S is to T. Let's confirm by computing two relative errors, the difference between X and S, and the difference between S and T.

format shorte relerr(1) = maxabs(X - S)/maxabs(T); relerr(2) = maxabs(S - T)/maxabs(T) relerrtotal = sum(relerr) relerr = 5.4143e-05 9.0252e-05 relerrtotal = 1.4439e-04

The error in the computed inverse comes from two sources -- generating the matrix in the first place and then computing the inverse. The first of these is actually larger than the second, although the two are comparable.

\n'); d.write(code_string); // Add copyright line at the bottom if specified. if (copyright.length > 0) { d.writeln(''); d.writeln('%%'); if (copyright.length > 0) { d.writeln('% _' + copyright + '_'); } } d.write('\n'); d.title = title + ' (MATLAB code)'; d.close(); } -->

Get the MATLAB code (requires JavaScript)

Published with MATLAB® R2017a

at Caltech in 1960. % It has been part of my professional life ever since. %% David Hilbert % Around the turn of the 20th century, David Hilbert was the world's % most famous mathematician. He introduced the matrix that now bears % his name in a paper in 1895. The elements of the matrix, which are % reciprocals of consecutive positive integers, are constant along the % antidiagonals. % % $$h_{i,j} = \frac{1}{i+j-1}, \ \ i,j = 1:n$$ %% format rat H5 = hilb(5) %% latex(sym(H5)); %% % $$H_5 = \left(\begin{array}{ccccc} % 1 & \frac{1}{2} & \frac{1}{3} & \frac{1}{4} & \frac{1}{5}\\ % \frac{1}{2} & \frac{1}{3} & \frac{1}{4} & \frac{1}{5} & \frac{1}{6}\\ % \frac{1}{3} & \frac{1}{4} & \frac{1}{5} & \frac{1}{6} & \frac{1}{7}\\ % \frac{1}{4} & \frac{1}{5} & \frac{1}{6} & \frac{1}{7} & \frac{1}{8}\\ % \frac{1}{5} & \frac{1}{6} & \frac{1}{7} & \frac{1}{8} & \frac{1}{9} % \end{array}\right)$$ %% % Here's a picture. The continuous surface generated is very smooth. H12 = hilb(12); surf(log(H12)) view(60,60) %% Least squares % Hilbert was interested in this matrix because it comes up in the % least squares approximation of a continuous function on the unit % interval by polynomials, expressed in the conventional basis % as linear combinations of monomials. % % $$p(x) = \sum_{j=1}^n c_j x^{j-1}$$ % % The coefficient matrix for the normal equations has elements % % $$\int_0^1 x^{i+j-2} dx \ = \ \frac{1}{i+j-1}$$ %% Properties % A Hilbert matrix has many useful properties. % % * Symmetric. % * Positive definite. % * Hankel, $a_{i,j}$ is a function of $i+j$. % * Cauchy, $a_{i,j} = 1/(x_i - y_j)$. % * Nearly singular. %% % Each column of a Hilbert matrix is nearly a multiple of % the other columns. So the columns are nearly linearly % dependent and the matrix is close to, but not exactly, singular. %% hilb % MATLAB has always had functions |hilb| and |invhilb| that compute % the Hilbert matrix and its inverse. The body of |hilb| is now only % two lines. % % J = 1:n; % H = 1./(J'+J-1); % % We often cite this as a good example of _singleton expansion_. % A column vector is added to a row vector to produce a matrix, % then a scalar is subtracted from that matrix, and finally the % reciprocals of the elements produce the result. %% Inverse % It is possible to express the elements of the inverse of the Hilbert % matrix in terms of binomial coefficients. For reasons that I've now % forgotten, I always use $T$ for $H^{-1}$. % % $$t_{i,j} = (-1)^{i+j} (i+j-1) {{n+i-1} \choose {n-j}} % {{n+j-1} \choose {n-i}} {{i+j-2} \choose {i-1}}^2$$ %% % The elements of the inverse Hilbert matrix are integers, but they % are _large_ integers. The largest ones are on the diagonal. For % order 13, the largest element is % % $$(T_{13})_{9,9} \ = \ 100863567447142500$$ % % This is over $10^{17}$ and is larger than double precision |flintmax|. format longe flintmax = flintmax %% % So, it is possible to represent the largest % elements exactly only if $n \le 12$. %% % The HELP entry for |invhilb| includes a sentence inherited from % my original Fortran MATLAB. % % The result is exact for N less than about 15. % % Now that's misleading. It should say % % The result is exact for N <= 12. % % (I'm filing a bug report.) %% invhilv % The body of |invhilb| begins by setting |p| to the order |n|. % The doubly nested |for| loops then evaluate the binomial coefficient % formula recursively, avoiding unnecessary integer overflow. dbtype invhilb 18:28 %% % I first programmed this algorithm in machine language for the Burroughs % 205 Datatron at Caltech almost 60 years ago. I was barely out of my % teens. %% % Here's the result for |n = 6|. format short T6 = invhilb(6) %% % A checkerboard sign pattern with large integers in the inverse cancels % the smooth surface of the Hilbert matrix itself. T12 = invhilb(12); spy(T12 > 0) %% % A log scale mitigates the jaggedness. surf(sign(T12).*log(abs(T12))) view(60,60) %% Rookie experiment % Now using MATLAB, I am going to repeat the experiment that I did on % the Burroughs 205 when I was still a rookie. I had just written my % first program that used Gaussian elimination to invert matrices. % I proceeded to test it by inverting Hilbert matrices and comparing % the results with the exact inverses. (Today's code uses % this utility function that picks out the largest element in % a matrix.) maxabs = @(X) max(double(abs(X(:)))); %% % Here is |n = 10|. n = 10 H = hilb(n); X = inv(H); % Computed inverse T = invhilb(n); % Theoretical inverse E = X - T; % The error err = maxabs(E) %% % At first I might have said: % % _Wow! The error is $10^8$. That's a pretty big error. % Can I trust my matrix inversion code? What went wrong?_ %% % But I knew the elements of the inverse are huge. % We should be looking at _relative_ error. relerr = maxabs(E)/maxabs(T) %% % _OK. The relative error is $10^{-4}$. That still seems like a lot._ %% % I knew that the Hilbert matrix is nearly singular. That's why I % was using it. John Todd was one of the first people to write about % condition numbers. An error estimate that involves nearness to % singularity and the floating point accuracy would be expressed today % by esterr = cond(H)*eps %% % That was about all I understood at the time. The roundoff error % in the inversion process is magnified by the condition number of % the matrix. And, the error I observe is less than the estimate that % this simple analysis provides. So my inversion code passes this test. %% Deeper explanation % I met % a few years later and came to realize that there is more to the story. % I'm not actually inverting the Hilbert matrix. There are roundoff % errors involved in computing |H| even before it is passed to the % inversion routine. %% % Today, the Symbolic Math Toolbox helps provide a deeper explanation. % The |'f'| flag on the |sym| constructor says to convert double precision % floating point arguments exactly to their rational representation. % Here's how it works in this situation. To save space, I'll print just % the first column. H = hilb(n); F = sym(H,'f'); F(:,1) %% % The elements of |H| that are not exact binary fractions become ratios % of large integers. The denominators are powers of two; in this case % $2^{54}$ and $2^{55}$. The numerators are these denominators divided by % $3$, $5$, etc. and then rounded to the nearest integer. % The elements of |F| are as close to the exact elements of a Hilbert % matrix as we can get in binary floating point. %% % Let's invert |F|, using the exact rational arithmetic provided % by the Symbolic Toolbox. (I couldn't do this in 1960.) S = inv(F); %% % We now have three inverse Hilbert matrices, |X|, |S|, and |T|. % % * |X| is the approximate inverse computed with floating point arithmetic % by the routine I was testing years ago, or by MATLAB |inv| function % today. % * |S| is the exact inverse of the floating point matrix % that was actually passed to the inversion routine. % % * |T| is the exact Hilbert inverse, obtained from the binomial % coefficient formula. %% % Let's print the first columns alongside each other. fprintf('%12s %16s %15s\n','X','S','T') fprintf('%16.4f %16.4f %12.0f\n',[X(:,1) S(:,1) T(:,1)]') %% % It looks like |X| is closer to |S| than |S| is to |T|. Let's confirm % by computing two relative errors, the difference between % |X| and |S|, and the difference between |S| and |T|. format shorte relerr(1) = maxabs(X - S)/maxabs(T); relerr(2) = maxabs(S - T)/maxabs(T) relerrtotal = sum(relerr) %% % The error in the computed inverse comes from two sources REPLACE_WITH_DASH_DASH generating % the matrix in the first place and then computing the inverse. % The first of these is actually larger than the second, although % the two are comparable. ##### SOURCE END ##### b5980438b46c4cb5acd20123a1a034fd -->

### Using Machine Learning to Predict Epileptic Seizures from EEG Data

latest technical articles - Fri, 06/02/2017 - 05:30
This article describes how a Kaggle competition winner trained classification models that could predict epileptic seizures from human intracranial electroencephalograph (EEG) recordings.

### MATLAB and Simulink in the World: Data Analytics

latest technical articles - Wed, 05/31/2017 - 05:30
Engineers and scientists use MATLAB to build predictive models, compare machine learning algorithms, and integrate data analytics into production systems.

### Quadruple Precision, 128-bit Floating Point Arithmetic

latest Blogs - Tue, 05/23/2017 - 02:18

The floating point arithmetic format that occupies 128 bits of storage is known as binary128 or quadruple precision. This blog post describes an implementation of quadruple precision programmed entirely in the MATLAB language.

ContentsBackground

The IEEE 754 standard, published in 1985, defines formats for floating point numbers that occupy 32 or 64 bits of storage. These formats are known as binary32 and binary64, or more frequently as single and double precision. For many years MATLAB used only double precision and it remains our default format. Single precision has been added gradually over the last several years and is now also fully supported.

A revision of IEEE 754, published in 2008, defines two more floating point formats. One, binary16 or half precision, occupies only 16 bits and was the subject of my previous blog post. It is primarily intended to reduce storage and memory bandwidth requirements. Since it provides only "half" precision, its use for actual computation is problematic.

The other new format introduced in IEEE 754-2008 is binary128 or quadruple precision. It is intended for situations where the accuracy or range of double precision is inadequate.

I see two descriptions of quadruple precision software implementations on the Web.

I have not used either package, but judging by their Web pages, they both appear to be complete and well supported.

The MATLAB Symbolic Math Toolbox provides vpa, arbitrary precision decimal floating point arithmetic, and sym, exact rational arithmetic. Both provide accuracy and range well beyond quadruple precision, but do not specifically support the 128-bit IEEE format.

My goal here is to describe a prototype of a MATLAB object, fp128, that implements quadruple precision with code written entirely in the MATLAB language. It is not very efficient, but is does allow experimentation with the 128-bit format.

Beyond double

There are other floating point formats beyond double precision. Long double usually refers to the 80-bit extended precision floating point registers available with the Intel x86 architecture and described as double extended in IEEE 754. This provides the same exponent range as quadruple precision, but much less accuracy.

Double double refers to the use of a pair of double precision values. The exponent field and sign bit of the second double are ignored, so this is effectively a 116-bit format. Both the exponent range and the precision are more than double but less than quadruple.

Floating point anatomy

The format of a floating point number is characterized by two parameters, p, the number of bits in the fraction and q, the number of bits in the exponent. I will compare four precisions, half, single, double, and quadruple. The four pairs of characterizing parameters are

p = [10, 23, 52 112]; q = [5, 8, 11, 15];

With these values of p and q, and with one more bit for the sign, the total number of bits in the word, w, is a power of two.

format shortg w = p + q + 1 w = 16 32 64 128

Normalized numbers

Most floating point numbers are normalized, and are expressed as

$$x = \pm (1+f)2^e$$

The fraction $f$ is in the half open interval

$$0 \leq f < 1$$

The binary representation of $f$ requires at most p bits. In other words $2^p f$ is an integer in the range

$$0 \leq 2^p f < 2^p$$

The exponent $e$ is an integer in the range

$$-b+1 \leq e \leq b$$

The quantity $b$ is both the largest exponent and the bias.

$$b = 2^{q-1} - 1$$

b = 2.^(q-1)-1 b = 15 127 1023 16383

The fractional part of a normalized number is $1+f$, but only $f$ needs to be stored. That leading $1$ is known as the hidden bit.

Subnormal

There are two values of the exponent $e$ for which the biased exponent, $e+b$, reaches the smallest and largest values possible to represent in q bits. The smallest is

$$e + b = 0$$

The corresponding floating point numbers do not have a hidden leading bit. These are the subnormal or denormal numbers.

$$x = \pm f 2^{-b}$$

Infinity and Not-A-Number

The largest possible biased exponent is

$$e + b = 2^q-1$$.

Quantities with this exponent field represent infinities and NaN, or Not-A-Number.

The percentage of floating point numbers that are exceptional because they are subnormal, infinity or NaN increases as the precision decreases. Exceptional exponents are only $2$ values out of $2^q$. For quadruple precision this is $2/2^{15}$, which is less than a one one-thousandth of one percent.

Encode the sign bit with s = 0 for nonnegative and s = 1 for negative. And encode the exponent with an offsetting bias, b. Then a floating point number can be packed in w bits with

x = [s e+b 2^p*f]Precision and range

epsilon

If a real number cannot be expressed with a binary expansion requiring at most p bits, it must be approximated by a floating point number that does have such a binary representation. This is roundoff error. The important quantity characterizing precision is machine epsilon, or eps. In MATLAB, eps(x) is the distance from x to the next larger (in absolute value) floating point number (of that class). With no argument, eps is simply the difference between 1 and the next larger floating point number.

format shortg eps = 2.^(-p) eps = 0.00097656 1.1921e-07 2.2204e-16 1.9259e-34

This tells us that quadruple precision is good for about 34 decimal digits of accuracy, double for about 16 decimal digits, single for about 7, and half for about 3.

realmax

If a real number, or the result of an arithmetic operation, is too large to be represented, it overflows and is replaced by infinity. The largest floating point number that does not overflow is realmax. When I try to compute quadruple realmax with double precision, it overflows. I will fix this up in the table to follow.

realmax = 2.^b.*(2-eps) realmax = 65504 3.4028e+38 1.7977e+308 Inf

realmin

Underflow and representation of very small numbers is more complicated. The smallest normalized floating point number is realmin. When I try to compute quadruple realmin it underflows to zero. Again, I will fix this up in the table.

realmin = 2.^(-b+1) realmin = 6.1035e-05 1.1755e-38 2.2251e-308 0

tiny

But there are numbers smaller than realmin. IEEE 754 introduced the notion of gradual underflow and denormal numbers. In the 2008 revised standard their name was changed to subnormal.

Think of roundoff in numbers near underflow. Before 754, floating point numbers had the disconcerting property that x and y could be unequal, but their difference could underflow, so x-y becomes 0. With 754 the gap between 0 and realmin is filled with numbers whose spacing is the same as the spacing between realmin and 2*realmin. I like to call this spacing, and the smallest subnormal number, tiny.

tiny = realmin.*eps tiny = 5.9605e-08 1.4013e-45 4.9407e-324 0 Floating point integers

flintmax

It is possible to do integer arithmetic with floating point numbers. I like to call such numbers flints. When we write the numbers $3$ and $3.0$, they are different descriptions of the same integer, but we think of one as fixed point and the other as floating point. The largest flint is flintmax.

flintmax = 2./eps flintmax = 2048 1.6777e+07 9.0072e+15 1.0385e+34

Technically all the floating point numbers larger than flintmax are integers, but the spacing between them is larger than one, so it is not safe to use them for integer arithmetic. Only integer-valued floating point numbers between 0 and flintmax are allowed to be called flints.

Table

Let's collect all these anatomical characteristics together in a new MATLAB table. I have now edited the output and inserted the correct quadruple precision values.

T = [w; p; q; b; eps; realmax; realmin; tiny; flintmax]; T = table(T(:,1), T(:,2), T(:,3), T(:,4), ... 'variablenames',{'half','single','double','quadruple'}, ... 'rownames',{'w','p','q','b','eps','realmax','realmin', ... 'tiny','flintmax'}); type Table.txt half single double quadruple __________ __________ ___________ __________ w 16 32 64 128 p 10 23 52 112 q 5 8 11 15 b 15 127 1023 16383 eps 0.00097656 1.1921e-07 2.2204e-16 1.9259e-34 realmax 65504 3.4028e+38 1.7977e+308 1.190e+4932 realmin 6.1035e-05 1.1755e-38 2.2251e-308 3.362e-4932 tiny 5.9605e-08 1.4013e-45 4.9407e-324 6.475e-4966 flintmax 2048 1.6777e+07 9.0072e+15 1.0385e+34 fp128

I am currently working on code for an object, @fp128, that could provide a full implementation of quadruple-precision arithmetic. The methods available so far are

methods(fp128) Methods for class fp128: abs eq le mtimes realmax subsref cond fp128 lt ne realmin svd diag frac lu norm shifter sym disp ge max normalize sig times display gt minus normalize2 sign tril double hex mldivide plus sqrt triu ebias hypot mpower power subsasgn uminus eps ldivide mrdivide rdivide subsindex

The code that I have for quadrule precision is much more complex than the code that I have for half precision. There I am able to "cheat" by converting half precision numbers to doubles and relying on traditional MATLAB arithmetic. I can't do that for quads.

The storage scheme for quads is described in the help entry for the constructor.

help @fp128/fp128 fp128 Quad precision constructor. z = fp128(x) has three fields. x = s*(1+f)*2^e, where z.s, one uint8, s = (-1)^sg = 1-2*sg, sg = (1-s)/2. z.e, 15 bits, biased exponent, one uint16. b = 2^14-1 = 16383, eb = e + b, 1 <= eb <= 2*b for normalized quads, eb = 0 for subnormal quads, eb = 2*b+1 = 32767 for infinity and NaN. z.f, 112 bits, nonnegative fraction, 4-vector of uint64s, each with 1/4-th of the bits, 0 <= f(k) < 2^28, 4*28 = 112. z.f represents sum(f .* pow2), pow2 = 2.^(-28*(1:4)) Reference page in Doc Center doc fp128

Breaking the 112-bit fraction into four 28-bit pieces makes it possible to do arithmetic operations on the pieces without worrying about integer overflow. The core of the times code, which implements x.*y, is the convolution of the two fractional parts.

dbtype 45:53 @fp128/times 45 % The multiplication. 46 % z.f = conv(x.f,y.f); 47 % Restore hidden 1's. 48 xf = [1 x.f]; 49 yf = [1 y.f]; 50 zf = zeros(1,9,'uint64'); 51 for k = 1:5 52 zf(k:k+4) = zf(k:k+4) + yf(k)*xf; 53 end

The result of the convolution, zf, is a uint64 vector of length nine with 52-bit elements. It must be renormalized to the fit the fp128 storage scheme.

Addition and subtraction involve addition and subtraction of the fractional parts after they have been shifted so that the corresponding exponents are equal. Again, this produces temporary vectors that must be renormalized.

Scalar division, y/x, is done by first computing the reciprocal of the denominator, 1/x, and then doing one final multiplication, 1/x * y. The reciprocal is computed by a few steps of Newton iteration, starting with a scaled reciprocal, 1/double(x).

Examples

The output for each example shows the three fields in hexadecimal -- one sign field, one biased exponent field, and one fraction field that is a vector with four entries displayed with seven hex digits. This is followed by a 36 significant digit decimal representation.

One

clear format long one = fp128(1) one = 0 3fff 0000000 0000000 0000000 0000000 1.0

eps

eps = eps(one) eps = 0 3f8f 0000000 0000000 0000000 0000000 0.000000000000000000000000000000000192592994438723585305597794258492732

1 + eps

one_plus_eps = one + eps one_plus_eps = 0 3fff 0000000 0000000 0000000 0000001 1.00000000000000000000000000000000019

2 - eps

two_minus_eps = 2 - eps two_minus_eps = 0 3fff fffffff fffffff fffffff fffffff 1.99999999999999999999999999999999981

realmin

rmin = realmin(one) rmin = 0 0001 0000000 0000000 0000000 0000000 3.3621031431120935062626778173217526e-4932

realmax

rmax = realmax(one) rmax = 0 7ffe fffffff fffffff fffffff fffffff 1.18973149535723176508575932662800702e4932

Compute 1/10 with double, then convert to quadruple.

dble_tenth = fp128(1/10) dble_tenth = 0 3ffb 9999999 99999a0 0000000 0000000 0.100000000000000005551115123125782702

quad_tenth = 1/fp128(10) quad_tenth = 0 3ffb 9999999 9999999 9999999 9999999 0.0999999999999999999999999999999999928

Double precision pi converted to quadruple.

dble_pi = fp128(pi) dble_pi = 0 4000 921fb54 442d180 0000000 0000000 3.14159265358979311599796346854418516

quad_pi = fp128(sym('pi')) quad_pi = 0 4000 921fb54 442d184 69898cc 51701b8 3.1415926535897932384626433832795028 Matrix operations

The 4-by-4 magic square from Durer's Melancholia II provides my first matrix example.

clear M = fp128(magic(4));

Let's see how the 128-bit elements look in hex.

format hex M M = 0 4003 0000000 0000000 0000000 0000000 0 4001 4000000 0000000 0000000 0000000 0 4002 2000000 0000000 0000000 0000000 0 4001 0000000 0000000 0000000 0000000 0 4000 0000000 0000000 0000000 0000000 0 4002 6000000 0000000 0000000 0000000 0 4001 c000000 0000000 0000000 0000000 0 4002 c000000 0000000 0000000 0000000 0 4000 8000000 0000000 0000000 0000000 0 4002 4000000 0000000 0000000 0000000 0 4001 8000000 0000000 0000000 0000000 0 4002 e000000 0000000 0000000 0000000 0 4002 a000000 0000000 0000000 0000000 0 4002 0000000 0000000 0000000 0000000 0 4002 8000000 0000000 0000000 0000000 0 3fff 0000000 0000000 0000000 0000000

Check that the row sums are all equal. This matrix-vector multiply can be done exactly with the flints in the magic square.

e = fp128(ones(4,1)) Me = M*e e = 0 3fff 0000000 0000000 0000000 0000000 0 3fff 0000000 0000000 0000000 0000000 0 3fff 0000000 0000000 0000000 0000000 0 3fff 0000000 0000000 0000000 0000000 Me = 0 4004 1000000 0000000 0000000 0000000 0 4004 1000000 0000000 0000000 0000000 0 4004 1000000 0000000 0000000 0000000 0 4004 1000000 0000000 0000000 0000000 Quadruple precision backslash

I've overloaded mldivide, so I can solve linear systems and compute inverses. The actual computation is done by lutx, a "textbook" function that I wrote years ago, long before this quadruple-precision project, followed by the requisite solution of triangular systems. But now the MATLAB object system insures that every individual arithmetic operation is done with IEEE 754 quadruple precision.

Let's generate a 3-by-3 matrix with random two-digit integer entries.

A = fp128(randi(100,3,3)) A = 0 4002 0000000 0000000 0000000 0000000 0 4001 8000000 0000000 0000000 0000000 0 4004 b000000 0000000 0000000 0000000 0 4005 3800000 0000000 0000000 0000000 0 4005 7800000 0000000 0000000 0000000 0 4002 a000000 0000000 0000000 0000000 0 4004 c800000 0000000 0000000 0000000 0 4004 7800000 0000000 0000000 0000000 0 4000 0000000 0000000 0000000 0000000

I am going to use fp128 backslash to invert A. So I need the identity matrix in quadruple precision.

I = fp128(eye(size(A)));

Now the overloaded backslash calls lutx, and then solves two triangular systems to produce the inverse.

X = A\I X = 0 3ff7 2fd38ea bcfb815 69cdccc a36d8a5 1 3ff9 c595b53 8c842ee f26189c a0770d4 0 3ffa c0bc8b7 4adcc40 4ea66ca 61f1380 1 3ff7 a42f790 e4ad874 c358882 7ff988e 0 3ffa 12ea8c2 3ef8c17 01c7616 5e03a5a 1 3ffa 70d4565 958740b 78452d8 f32d866 0 3ff9 2fd38ea bcfb815 69cdccc a36d8a7 0 3ff3 86bc8e5 42ed82a 103d526 a56452f 1 3ff6 97f9949 ba961b3 72d69d9 4ace666

Compute the residual.

AX = A*X R = I - AX; format short RD = double(R) AX = 0 3fff 0000000 0000000 0000000 0000000 0 3f90 0000000 0000000 0000000 0000000 1 3f8d 0000000 0000000 0000000 0000000 0 0000 0000000 0000000 0000000 0000000 0 3fff 0000000 0000000 0000000 0000000 0 3f8d 8000000 0000000 0000000 0000000 1 3f8c 0000000 0000000 0000000 0000000 1 3f8d 0000000 0000000 0000000 0000000 0 3ffe fffffff fffffff fffffff ffffffb RD = 1.0e-33 * 0 0 0.0241 -0.3852 0 0.0481 0.0481 -0.0722 0.4815

Both AX and R are what I expect from arithmetic that is accurate to about 34 decimal digits.

Although I get a different random A every time I publish this blog post, I expect that it has a modest condition number.

kappa = cond(A) kappa = 0 4002 7e97c18 91278cd 8375371 7915346 11.9560249020758193065358323606886569

Since A is not badly conditioned, I can invert the computed inverse and expect to get close to the original integer matrix. The elements of the resulting Z are integers, possibly bruised with quadruple precision fuzz.

format hex Z = X\I Z = 0 4002 0000000 0000000 0000000 0000000 0 4001 8000000 0000000 0000000 0000000 0 4004 b000000 0000000 0000000 0000004 0 4005 37fffff fffffff fffffff ffffffc 0 4005 77fffff fffffff fffffff ffffffe 0 4002 a000000 0000000 0000000 0000001 0 4004 c7fffff fffffff fffffff ffffffc 0 4004 77fffff fffffff fffffff ffffffc 0 3fff fffffff fffffff fffffff ffffffc Quadruple precision SVD

I have just nonchalantly computed cond(A). Here is the code for the overloaded cond.

type @fp128/cond.m function kappa = cond(A) sigma = svd(A); kappa = sigma(1)/sigma(end); end

So it is correctly using the singular value decomposition. I also have svd overloaded. The SVD computation is handled by a 433 line M-file, svdtx, that, like lutx, was written before fp128 existed. It was necessary to modify five lines in svdtx. The line

u = zeros(n,ncu);

u = fp128(zeros(n,ncu));

Similarly for v, s, e and work. I should point out that the preallocation of the arrays is inherited from the LINPACK Fortran subroutine DSVDC. Without it, svdtx would not have required any modification to work correctly in quadruple precision.

Let's compute the full SVD.

[U,S,V] = svd(A) U = 1 3ffe 57d9492 76f3ea4 dc14bb3 15d42c1 1 3ffe 75a77c4 8c7b469 2cac695 59be7fe 1 3ffc 0621737 9b04c78 1c2109d 8736b46 1 3ffb 38214c0 d75c84c 4bcf5ff f3cffd7 1 3ffb a9281e3 e12dd3a d632d61 c8f6e60 0 3ffe fbbccdc a571fa1 f5a588b fb0d806 1 3ffe 79587db 4889548 f09ae4b cd0150c 0 3ffe 59fae16 17bcabb 6408ba4 7b2a573 0 3ff8 cde38fc e952ad5 8b526c2 780c2e5 S = 0 4006 1f3ad79 d0b9b08 18b1444 030e4ef 0 0000 0000000 0000000 0000000 0000000 0 0000 0000000 0000000 0000000 0000000 0 0000 0000000 0000000 0000000 0000000 0 4004 a720ef6 28c6ec0 87f4c54 82dda2a 0 0000 0000000 0000000 0000000 0000000 0 0000 0000000 0000000 0000000 0000000 0 0000 0000000 0000000 0000000 0000000 0 4002 8061b9a 0e96d8c c2ef745 9ea4c9a V = 1 3ffb db3df03 9b5e1b3 5bf4478 0e42b0d 1 3ffe b540007 4d4bc9e dc9461a 0de0481 1 3ffe 03aaff4 d9cea2c e8ee2bc 2eba908 0 3ffe fa73e09 9ef8810 a03d2eb 46ade00 1 3ffa b316e2f fe9d3ae dfa9988 fbca927 1 3ffc 184af51 f25fece 97bc0da 5ff13a2 1 3ffb 706955f a877cbb b63f6dd 4e2150e 0 3ffe 08fc1eb 7b86ef7 4af3c6c 732aae9 1 3ffe b3aaead ef356e2 7cd2937 94b22a7

Reconstruct A from its quadruple precision SVD. It's not too shabby.

USVT = U*S*V' USVT = 0 4001 fffffff fffffff fffffff fffffce 0 4001 7ffffff fffffff fffffff fffffc7 0 4004 b000000 0000000 0000000 000000a 0 4005 37fffff fffffff fffffff ffffff1 0 4005 77fffff fffffff fffffff ffffff6 0 4002 9ffffff fffffff fffffff fffffd2 0 4004 c7fffff fffffff fffffff ffffff1 0 4004 77fffff fffffff fffffff ffffff4 0 3fff fffffff fffffff fffffff ffffe83 Rosser matrix

An interesting example is provided by a classic test matrix, the 8-by-8 Rosser matrix. Let's compare quadruple precision computation with the exact rational computation provided by the Symbolic Math Toolbox.

First, generate quad and sym versions of rosser.

R = fp128(rosser); S = sym(rosser) S = [ 611, 196, -192, 407, -8, -52, -49, 29] [ 196, 899, 113, -192, -71, -43, -8, -44] [ -192, 113, 899, 196, 61, 49, 8, 52] [ 407, -192, 196, 611, 8, 44, 59, -23] [ -8, -71, 61, 8, 411, -599, 208, 208] [ -52, -43, 49, 44, -599, 411, 208, 208] [ -49, -8, 8, 59, 208, 208, 99, -911] [ 29, -44, 52, -23, 208, 208, -911, 99]

R is symmetric, but not positive definite, so its LU factorization requires pivoting.

[L,U,p] = lutx(R); format short p p = 1 2 3 7 6 8 4 5

R is singular, so with exact computation U(n,n) would be zero. With quadruple precision, the diagonal of U is

format long e diag(U) ans = 611.0 836.126022913256955810147299509001582 802.209942588471107300640276546225738 99.0115741407236314604636000423592687 -710.481057851148425133280246646085002 579.272484693223512196223933017062413 -1.2455924519190846395771824210276321 0.000000000000000000000000000000215716190833522835766351129431653015

The relative size of the last diagonal element is zero to almost 34 digits.

double(U(8,8)/U(1,1)) ans = 3.530543221497919e-34

Compare this with symbolic computation, which, in this case, can compute an LU decomposition with exact rational arithmetic and no pivoting.

[L,U] = lu(S); diag(U) ans = 611 510873/611 409827400/510873 50479800/2049137 3120997/10302 -1702299620/3120997 255000/40901 0

As expected, with symbolic computation U(8,8) is exactly zero.

r = svd(R) r = 1020.04901842999682384631379130551006 1020.04901842999682384631379130550858 1019.99999999999999999999999999999941 1019.90195135927848300282241090227735 999.999999999999999999999999999999014 999.999999999999999999999999999998817 0.0980486407215169971775890977220345302 0.0000000000000000000000000000000832757192990287779822645036097560521

The Rosser matrix is atypical because its characteristic polynomial factors over the rationals. So, even though it is of degree 8, the singular values are the roots of quadratic factors.

s = svd(S) s = 10*10405^(1/2) 10*10405^(1/2) 1020 10*(1020*26^(1/2) + 5201)^(1/2) 1000 1000 10*(5201 - 1020*26^(1/2))^(1/2) 0

The relative error of the quadruple precision calculation.

double(norm(r - s)/norm(s)) ans = 9.293610246879066e-34

Postscript

Finally, verify that we've been working all this time with fp128 and sym objects.

whos Name Size Bytes Class Attributes A 3x3 3531 fp128 AX 3x3 3531 fp128 I 3x3 3531 fp128 L 8x8 8 sym M 4x4 6128 fp128 Me 4x1 1676 fp128 R 8x8 23936 fp128 RD 3x3 72 double S 8x8 8 sym U 8x8 8 sym USVT 3x3 3531 fp128 V 3x3 3531 fp128 X 3x3 3531 fp128 Z 3x3 3531 fp128 ans 1x1 8 double e 4x1 1676 fp128 kappa 1x1 563 fp128 p 8x1 64 double r 8x1 3160 fp128 s 8x1 8 sym \n'); d.write(code_string); // Add copyright line at the bottom if specified. if (copyright.length > 0) { d.writeln(''); d.writeln('%%'); if (copyright.length > 0) { d.writeln('% _' + copyright + '_'); } } d.write('\n'); d.title = title + ' (MATLAB code)'; d.close(); } -->

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Published with MATLAB® R2017a

> %% Background % The IEEE 754 standard, published in 1985, defines formats for % floating point numbers that occupy 32 or 64 bits of storage. % These formats are known as _binary32_ and _binary64_, or more % frequently as _single_ and _double precision_. For many years % MATLAB used only double precision and it remains our default % format. Single precision has been added gradually % over the last several years and is now also fully supported. %% % A revision of IEEE 754, published in 2008, defines two more floating % point formats. One, _binary16_ or _half precision_, occupies only % 16 bits and was the subject of my % . It is primarily intended to reduce storage and % memory bandwidth requirements. Since it provides only "half" precision, % its use for actual computation is problematic. %% % The other new format introduced in IEEE 754-2008 is _binary128_ or % _quadruple precision_. It is intended for situations where the accuracy % or range of double precision is inadequate. %% % I see two descriptions of quadruple precision % software implementations on the Web. % % * % % * %% % I have not used either package, but judging by their Web pages, they % both appear to be complete and well supported. %% % The MATLAB Symbolic Math Toolbox provides |vpa|, arbitrary precision % decimal floating point arithmetic, and |sym|, exact rational arithmetic. % Both provide accuracy and range well beyond quadruple precision, but do % not specifically support the 128-bit IEEE format. %% % My goal here is to describe a prototype of a MATLAB object, |fp128|, % that implements quadruple precision with code written entirely in the % MATLAB language. It is not very efficient, but is does allow % experimentation with the 128-bit format. %% Beyond double % There are other floating point formats beyond double precision. % _Long double_ usually refers to the 80-bit extended precision % floating point registers available with the Intel x86 architecture % and described as _double extended_ in IEEE 754. This provides % the same exponent range as quadruple precision, but much less % accuracy. %% % _Double double_ refers to the use of a pair of double precision % values. The exponent field and sign bit of the second double are % ignored, so this is effectively a 116-bit format. Both the exponent % range and the precision are more than double but less than quadruple. %% Floating point anatomy % The format of a floating point number is characterized by two % parameters, |p|, the number of bits in the fraction and |q|, the number % of bits in the exponent. I will compare four precisions, % _half_, _single_, _double_, and _quadruple_. % The four pairs of characterizing parameters are p = [10, 23, 52 112]; %% q = [5, 8, 11, 15]; %% % With these values of |p| and |q|, and with one more bit for the sign, % the total number of bits in the word, |w|, is a power of two. format shortg w = p + q + 1 %% % *Normalized numbers* % % Most floating point numbers are _normalized_, and are expressed as % % $$x = \pm (1+f)2^e$$ %% % The _fraction_ $f$ is in the half open interval % % $$0 \leq f < 1$$ % %% % The binary representation of $f$ requires at most |p| bits. % In other words $2^p f$ is an integer in the range % % $$0 \leq 2^p f < 2^p$$ %% % % The _exponent_ $e$ is an integer in the range % % $$-b+1 \leq e \leq b$$ % %% % The quantity $b$ is both the largest exponent and the |bias|. % % $$b = 2^{q-1} - 1$$ b = 2.^(q-1)-1 %% % The fractional part of a normalized number is $1+f$, but only $f$ % needs to be stored. That leading $1$ is known as the _hidden bit_. %% % *Subnormal* % % There are two values of the exponent $e$ for which the biased exponent, % $e+b$, reaches the smallest and largest values possible to represent % in |q| bits. The smallest is % % $$e + b = 0$$ % % The corresponding floating point numbers do not have a hidden leading % bit. These are the _subnormal_ or _denormal_ numbers. % % $$x = \pm f 2^{-b}$$ % %% % *Infinity and Not-A-Number* % % The largest possible biased exponent is % % $$e + b = 2^q-1$$. % % Quantities with this exponent field represent _infinities_ and % _NaN_, or _Not-A-Number_. %% % The percentage of floating point numbers that are exceptional % because they are subnormal, infinity or NaN increases as the % precision decreases. Exceptional exponents are only $2$ values % out of $2^q$. For quadruple precision this is $2/2^{15}$, which is % less than a one one-thousandth of one percent. %% % Encode the sign bit with |s = 0| for nonnegative and |s = 1| for % negative. And encode the exponent with an offsetting bias, |b|. % Then a floating point number can be packed in |w| bits with % % x = [s e+b 2^p*f] %% Precision and range % *epsilon* % % If a real number cannot be expressed with a binary expansion requiring % at most |p| bits, it must be approximated by a floating point number % that does have such a binary representation. This is _roundoff error_. % The important quantity characterizing precision is _machine epsilon_, % or |eps|. In MATLAB, |eps(x)| is the distance from |x| to the % next larger (in absolute value) floating point number (of that class). % With no argument, |eps| is simply the difference between |1| and the % next larger floating point number. format shortg eps = 2.^(-p) %% % This tells us that quadruple precision is good for about 34 decimal % digits of accuracy, double for about 16 decimal digits, single for % about 7, and half for about 3. %% % *realmax* % % If a real number, or the result of an arithmetic operation, is too % large to be represented, it _overflows_ and is replaced by _infinity_. % The largest floating point number that does not overflow is |realmax|. % When I try to compute quadruple |realmax| with double precision, % it overflows. I will fix this up in the table to follow. realmax = 2.^b.*(2-eps) %% % *realmin* % % _Underflow_ and representation of very small numbers is more complicated. % The smallest normalized floating point number is |realmin|. When I % try to compute quadruple |realmin| it underflows to zero. Again, % I will fix this up in the table. realmin = 2.^(-b+1) %% % *tiny* % % But there are numbers smaller than |realmin|. IEEE 754 introduced the % notion of _gradual underflow_ and _denormal_ numbers. In the 2008 % revised standard their name was changed to _subnormal_. %% % Think of roundoff in numbers near underflow. Before 754, floating % point numbers had the disconcerting property that |x| and |y| could % be unequal, but their difference could underflow, so |x-y| becomes |0|. % With 754 the gap between |0| and |realmin| is filled with numbers whose % spacing is the same as the spacing between |realmin| and |2*realmin|. % I like to call this spacing, and the smallest subnormal number, |tiny|. tiny = realmin.*eps %% Floating point integers % *flintmax* % % It is possible to do integer arithmetic with floating point numbers. % I like to call such numbers _flints_. When we write the numbers $3$ % and $3.0$, they are different descriptions of the same integer, but % we think of one as fixed point and the other as floating point. % The largest flint is |flintmax|. flintmax = 2./eps %% % Technically all the floating point numbers larger than |flintmax| % are integers, but the spacing between them is larger than one, so it % is not safe to use them for integer arithmetic. Only integer-valued % floating point numbers between |0| and |flintmax| are allowed to be % called flints. %% Table % Let's collect all these anatomical characteristics together in a % new MATLAB |table|. I have now edited the output and inserted % the correct quadruple precision values. T = [w; p; q; b; eps; realmax; realmin; tiny; flintmax]; T = table(T(:,1), T(:,2), T(:,3), T(:,4), ... 'variablenames',{'half','single','double','quadruple'}, ... 'rownames',{'w','p','q','b','eps','realmax','realmin', ... 'tiny','flintmax'}); type Table.txt %% fp128 % I am currently working on code for an object, [email protected]|, % that could provide a full implementation of quadruple-precision % arithmetic. The methods available so far are methods(fp128) %% % The code that I have for quadrule precision is much more complex than % the code that I have for % . There I am able to "cheat" by converting half % precision numbers to doubles and relying on traditional MATLAB % arithmetic. I can't do that for quads. %% % The storage scheme for quads is described in the help entry for % the constructor. help @fp128/fp128 %% % Breaking the 112-bit fraction into four 28-bit pieces makes it possible % to do arithmetic operations on the pieces without worrying about integer % overflow. The core of the |times| code, which implements |x.*y|, % is the convolution of the two fractional parts. dbtype 45:53 @fp128/times %% % The result of the convolution, |zf|, is a |uint64| vector of length % nine with 52-bit elements. It must be renormalized to the fit the % |fp128| storage scheme. %% % Addition and subtraction involve addition and subtraction of the % fractional parts after they have been shifted so that the % corresponding exponents are equal. Again, this produces temporary % vectors that must be renormalized. %% % Scalar division, |y/x|, is done by first computing the reciprocal % of the denominator, |1/x|, and then doing one final multiplication, % |1/x * y|. The reciprocal is computed by a few steps of Newton % iteration, starting with a scaled reciprocal, |1/double(x)|. %% Examples % The output for each example shows the three fields in hexadecimal REPLACE_WITH_DASH_DASH % one sign field, one biased exponent field, and one fraction field % that is a vector with four entries displayed with seven hex digits. % This is followed by a 36 significant digit decimal representation. %% % *One* clear format long one = fp128(1) %% % *eps* eps = eps(one) %% % *1 + eps* one_plus_eps = one + eps %% % *2 - eps* two_minus_eps = 2 - eps %% % *realmin* rmin = realmin(one) %% % *realmax* rmax = realmax(one) %% % *Compute 1/10 with double, then convert to quadruple.* dble_tenth = fp128(1/10) %% % *Compute 1/10 with quadruple.* quad_tenth = 1/fp128(10) %% % *Double precision |pi| converted to quadruple.* dble_pi = fp128(pi) %% % *|pi| accurate to quadruple precision.* quad_pi = fp128(sym('pi')) %% Matrix operations % The 4-by-4 magic square from Durer's Melancholia II provides my first % matrix example. clear M = fp128(magic(4)); %% % Let's see how the 128-bit elements look in hex. format hex M %% % Check that the row sums are all equal. This matrix-vector multiply % can be done exactly with the flints in the magic square. e = fp128(ones(4,1)) Me = M*e %% Quadruple precision backslash % I've overloaded |mldivide|, so I can solve linear systems and % compute inverses. The actual computation is done by |lutx|, % a "textbook" function that I wrote years ago, long before this % quadruple-precision project, followed by the requisite solution % of triangular systems. But now the MATLAB object system insures % that every individual arithmetic operation is done with IEEE 754 % quadruple precision. %% % Let's generate a 3-by-3 matrix with random two-digit integer entries. A = fp128(randi(100,3,3)) %% % I am going to use |fp128| backslash to invert |A|. % So I need the identity matrix in quadruple precision. I = fp128(eye(size(A))); %% % Now the overloaded backslash calls |lutx|, and then solves two % triangular systems to produce the inverse. X = A\I %% % Compute the residual. AX = A*X R = I - AX; format short RD = double(R) %% % Both |AX| and |R| are what I expect from arithmetic that is % accurate to about 34 decimal digits. %% % Although I get a different random |A| every time I publish this blog % post, I expect that it has a modest condition number. kappa = cond(A) %% % Since |A| is not badly conditioned, I can invert the computed inverse % and expect to get close to the original integer matrix. % The elements of the resulting |Z| are integers, possibly bruised with % quadruple precision fuzz. format hex Z = X\I %% Quadruple precision SVD % I have just nonchalantly computed |cond(A)|. Here is the code for % the overloaded |cond|. type @fp128/cond.m %% % So it is correctly using the singular value decomposition. I also % have |svd| overloaded. The SVD computation is handled by a 433 line % M-file, |svdtx|, that, like |lutx|, was written before |fp128| existed. % It was necessary to modify five lines in |svdtx|. The line % % u = zeros(n,ncu); % % had to be changed to % % u = fp128(zeros(n,ncu)); % % Similarly for |v|, |s|, |e| and |work|. I should point out that the % preallocation of the arrays is inherited from the LINPACK Fortran % subroutine DSVDC. Without it, |svdtx| would not have required any % modification to work correctly in quadruple precision. %% % Let's compute the full SVD. [U,S,V] = svd(A) %% % Reconstruct |A| from its quadruple precision SVD. It's not too shabby. USVT = U*S*V' %% Rosser matrix % An interesting example is provided by a classic test matrix, the 8-by-8 % . Let's compare quadruple precision computation with % the exact rational computation provided by the Symbolic Math Toolbox. %% % First, generate quad and sym versions of |rosser|. R = fp128(rosser); S = sym(rosser) %% % |R| is symmetric, but not positive definite, so its LU factorization % requires pivoting. [L,U,p] = lutx(R); format short p %% % |R| is singular, so with exact computation |U(n,n)| would be zero. % With quadruple precision, the diagonal of |U| is format long e diag(U) %% % The relative size of the last diagonal element is zero to almost % 34 digits. double(U(8,8)/U(1,1)) %% % Compare this with symbolic computation, which, in this case, can % compute an LU decomposition with exact rational arithmetic and no % pivoting. [L,U] = lu(S); diag(U) %% % As expected, with symbolic computation |U(8,8)| is exactly zero. %% % How about SVD? r = svd(R) %% % The Rosser matrix is atypical because its % characteristic polynomial factors over the rationals. So, even though % it is of degree 8, the singular values are the roots of % quadratic factors. s = svd(S) %% % The relative error of the quadruple precision calculation. double(norm(r - s)/norm(s)) %% % About 33 digits. %% Postscript % Finally, verify that we've been working all this time % with |fp128| and |sym| objects. whos ##### SOURCE END ##### d47b6ba7b4b7413ea76f76763559fea7 -->

### Developing PLC-Based Control and Management Systems for Electric and Diesel Multiple-Unit Railway Vehicles

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PESA engineers model low-level software requirements, run simulations to verify their designs, and generate IEC 61131-3 Structured Text for deployment to a programmable logic controller (PLC).

### Measuring the Impact of RF Impairments on an LTE System

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This example shows how to assess the impact of an RF front end on baseband modeling and standard measurements such as EVM.

### Building Deployable Applications for Evaluating PK/PD Drug Efficacy

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### Confirming the First-Ever Detection of Gravitational Waves by Analyzing Laser Interferometer Data

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### “Half Precision” 16-bit Floating Point Arithmetic

latest Blogs - Tue, 05/09/2017 - 02:30

The floating point arithmetic format that requires only 16 bits of storage is becoming increasingly popular. Also known as half precision or binary16, the format is useful when memory is a scarce resource.

ContentsBackground

The IEEE 754 standard, published in 1985, defines formats for floating point numbers that occupy 32 or 64 bits of storage. These formats are known as binary32 and binary64, or more frequently as single and double precision. For many years MATLAB used only double precision and it remains our default format. Single precision has been added gradually over the last several years and is now also fully supported.

A revision of IEEE 754, published in 2008, defines a floating point format that occupies only 16 bits. Known as binary16, it is primarily intended to reduce storage and memory bandwidth requirements. Since it provides only "half" precision, its use for actual computation is problematic. An interesting discussion of its utility as an image processing format with increased dynamic range is provided by Industrial Light and Magic. Hardware support for half precision is now available on many processors, including the GPU in the Apple iPhone 7. Here is a link to an extensive article about half precision on the NVIDIA GeForce GPU.

Floating point anatomy

The format of a floating point number is characterized by two parameters, p, the number of bits in the fraction and q, the number of bits in the exponent. I will consider four precisions, quarter, half, single, and double. The quarter-precision format is something that I just invented for this blog post; it is not standard and actually not very useful.

The four pairs of characterizing parameters are

p = [4, 10, 23, 52]; q = [3, 5, 8, 11];

With these values of p and q, and with one more bit for the sign, the total number of bits in the word, w, is a power of two.

w = p + q + 1 w = 8 16 32 64

Normalized numbers

Most floating point numbers are normalized, and are expressed as

$$x = \pm (1+f)2^e$$

The fraction $f$ is in the half open interval

$$0 \leq f < 1$$

The binary representation of $f$ requires at most p bits. In other words $2^p f$ is an integer in the range

$$0 \leq 2^p f < 2^p$$

The exponent $e$ is an integer in the range

$$-b+1 \leq e \leq b$$

The quantity $b$ is both the largest exponent and the bias.

$$b = 2^{q-1} - 1$$

b = 2.^(q-1)-1 b = 3 15 127 1023

The fractional part of a normalized number is $1+f$, but only $f$ needs to be stored. That leading $1$ is known as the hidden bit.

Subnormal

There are two values of the exponent $e$ for which the biased exponent, $e+b$, reaches the smallest and largest values possible to represent in q bits. The smallest is

$$e + b = 0$$

The corresponding floating point numbers do not have a hidden leading bit. These are the subnormal or denormal numbers.

$$x = \pm f 2^{-b}$$

Infinity and Not-A-Number

The largest possible biased exponent is

$$e + b = 2^q-1$$.

Quantities with this exponent field represent infinities and NaN, or Not-A-Number.

The percentage of floating point numbers that are exceptional because they are subnormal, infinity or NaN increases as the precision decreases. Exceptional exponents are only $2$ values out of $2^q$. For double precision this is $2/2^{11}$, which is less than a tenth of a percent, but for half precision it is $2/2^5$, which is more than 6 percent. And fully one-fourth of all my toy quarter precision floating point numbers are exceptional.

Encode the sign bit with s = 0 for nonnegative and s = 1 for negative. And encode the exponent with an offsetting bias, b. Then a floating point number can be packed in w bits with

x = [s e+b 2^p*f]Precision and range

epsilon

If a real number cannot be expressed with a binary expansion requiring at most p bits, it must be approximated by a floating point number that does have such a binary representation. This is roundoff error. The important quantity characterizing precision is machine epsilon, or eps. In MATLAB, eps(x) is the distance from x to the next larger (in absolute value) floating point number. With no argument, eps is simply the difference between 1 and the next larger floating point number.

format shortg eps = 2.^(-p) eps = 0.0625 0.00097656 1.1921e-07 2.2204e-16

This tells us that double precision is good for about 16 decimal digits of accuracy, single for about 7 decimal digits, half for about 3, and quarter for barely more than one.

realmax

If a real number, or the result of an arithmetic operation, is too large to be represented, it overflows and is replaced by the largest floating point number. This is

realmax = 2.^b.*(2-eps) realmax = 15.5 65504 3.4028e+38 1.7977e+308

realmin

Underflow and representation of very small numbers is more complicated. The smallest normalized floating point number is

realmin = 2.^(-b+1) realmin = 0.25 6.1035e-05 1.1755e-38 2.2251e-308

tiny

But there are numbers smaller than realmin. IEEE 754 introduced the notion of gradual underflow and denormal numbers. In the 2008 revised standard their name was changed to subnormal.

Think of roundoff in numbers near underflow. Before 754 floating point numbers had the disconcerting property that x and y could be unequal, but their difference could underflow so x-y becomes 0. With 754 the gap between 0 and realmin is filled with numbers whose spacing is the same as the spacing between realmin and 2*realmin. I like to call this spacing, and the smallest subnormal number, tiny.

tiny = realmin.*eps tiny = 0.015625 5.9605e-08 1.4013e-45 4.9407e-324 Floating point integers

flintmax

It is possible to do integer arithmetic with floating point numbers. I like to call such numbers flints. When we write the numbers $3$ and $3.0$, they are different descriptions of the same integer, but we think of one as fixed point and the other as floating point. The largest flint is flintmax.

flintmax = 2./eps flintmax = 32 2048 1.6777e+07 9.0072e+15

Technically all the floating point numbers larger than flintmax are integers, but the spacing between them is larger than one, so it is not safe to use them for integer arithmetic. Only integer-valued floating point numbers between 0 and flintmax are allowed to be called flints.

Table

Let's collect all these anatomical characteristics together in a new MATLAB table.

T = [w; p; q; b; eps; realmax; realmin; tiny; flintmax]; T = table(T(:,1), T(:,2), T(:,3), T(:,4), ... 'variablenames',{'quarter','half','single','double'}, ... 'rownames',{'w','p','q','b','eps','realmax','realmin', ... 'tiny','flintmax'}); disp(T) quarter half single double ________ __________ __________ ___________ w 8 16 32 64 p 4 10 23 52 q 3 5 8 11 b 3 15 127 1023 eps 0.0625 0.00097656 1.1921e-07 2.2204e-16 realmax 15.5 65504 3.4028e+38 1.7977e+308 realmin 0.25 6.1035e-05 1.1755e-38 2.2251e-308 tiny 0.015625 5.9605e-08 1.4013e-45 4.9407e-324 flintmax 32 2048 1.6777e+07 9.0072e+15 fp8 and fp16

Version 3.1 of Cleve's Laboratory includes code for objects @fp8 and @fp16 that begin to provide full implementations of quarter-precision and half-precision arithmetic.

The methods currently provided are

methods(fp16) Methods for class fp16: abs eps isfinite mrdivide rem subsref binary eq le mtimes round svd ctranspose fix lt ne sign tril diag fp16 lu norm single triu disp ge max plus size uminus display gt minus realmax subsasgn double hex mldivide realmin subsindex

These provide only partial implementations because the arithmetic is not done on the compact forms. We cheat. For each individual scalar operation, the operands are unpacked from their short storage into old fashioned doubles. The operation is then carried out by existing double precision code and the results returned to the shorter formats. This simulates the reduced precision and restricted range, but requires relatively little new code.

All of the work is done in the constructors @fp8/fp8.m and @fp16/fp16.m and what we might call the "deconstructors" @fp8/double.m and @fp16/double.m. The constructors convert ordinary floating point numbers to reduced precision representations by packing as many of the 32 or 64 bits as will fit into 8 or 16 bit words. The deconstructors do the reverse by unpacking things.

Once these methods are available, almost everything else is trivial. The code for most operations is like this one for the overloaded addition.

type @fp16/plus.m function z = plus(x,y) z = fp16(double(x) + double(y)); end Wikipedia test suite

0 01111 0000000000 = 1 0 00101 0000000000 = 2^-10 = eps 0 01111 0000000001 = 1+eps = 1.0009765625 (next smallest float after 1) 1 10000 0000000000 = -2 0 11110 1111111111 = 65504 (max half precision) = 2^15*(2-eps) 0 00001 0000000000 = 2^-14 = r ~ 6.10352e-5 (minimum positive normal) 0 00000 1111111111 = r*(1-eps) ~ 6.09756e-5 (maximum subnormal) 0 00000 0000000001 = r*eps ~ 5.96046e-8 (minimum positive subnormal) 0 00000 0000000000 ~ r*eps/2 (underflow to zero) 0 00000 0000000000 = 0 1 00000 0000000000 = -0 0 11111 0000000000 = infinity 1 11111 0000000000 = -infinity 0 11111 1111111111 = NaN 0 01101 0101010101 = 0.333251953125 ~ 1/3

This provides my test suite for checking fp16 operations on scalars.

clear zero = fp16(0); one = fp16(1); eps = eps(one); r = realmin(one); tests = {'1','eps','1+eps','-2','2/r*(2-eps)', ... 'r','r*(1-eps)','r*eps','r*eps/2', ... 'zero','-zero','1/zero','-1/zero','zero/zero','1/3'};

Let's run the tests.

for t = tests(:)' x = eval(t{:}); y = fp16(x); z = binary(y); w = double(y); fprintf(' %18s %04s %19.10g %19.10g %s\n', ... z,hex(y),double(x),w,t{:}) end 0 01111 0000000000 3C00 1 1 1 0 00101 0000000000 1400 0.0009765625 0.0009765625 eps 0 01111 0000000001 3C01 1.000976563 1.000976563 1+eps 1 10000 0000000000 C000 -2 -2 -2 0 11110 1111111111 7BFF 65504 65504 2/r*(2-eps) 0 00001 0000000000 0400 6.103515625e-05 6.103515625e-05 r 0 00000 1111111111 03FF 6.097555161e-05 6.097555161e-05 r*(1-eps) 0 00000 0000000001 0001 5.960464478e-08 5.960464478e-08 r*eps 0 00000 0000000001 0001 5.960464478e-08 5.960464478e-08 r*eps/2 0 00000 0000000000 0000 0 0 zero 0 00000 0000000000 0000 0 0 -zero 0 11111 0000000000 7C00 Inf Inf 1/zero 1 11111 0000000000 FC00 -Inf -Inf -1/zero 1 11111 1111111111 FFFF NaN NaN zero/zero 0 01101 0101010101 3555 0.3333333333 0.3332519531 1/3 Matrix operations

Most of the methods in @fp8 and @fp16 handle matrices. The 4-by-4 magic square from Durer's Melancholia II provides my first example.

clear format short M = fp16(magic(4)) M = 16 2 3 13 5 11 10 8 9 7 6 12 4 14 15 1

Let's see how the packed 16-bit elements look in binary.

B = binary(M) B = 4×4 string array Columns 1 through 3 "0 10011 0000000000" "0 10000 0000000000" "0 10000 1000000000" "0 10001 0100000000" "0 10010 0110000000" "0 10010 0100000000" "0 10010 0010000000" "0 10001 1100000000" "0 10001 1000000000" "0 10001 0000000000" "0 10010 1100000000" "0 10010 1110000000" Column 4 "0 10010 1010000000" "0 10010 0000000000" "0 10010 1000000000" "0 01111 0000000000"

Check that the row sums are all equal. This matrix-vector multiply can be done exactly with the flints in the magic square.

e = fp16(ones(4,1)) Me = M*e e = 1 1 1 1 Me = 34 34 34 34 fp16 backslash

I've overloaded mldivide, so I can solve linear systems and compute inverses. The actual computation is done by lutx, a "textbook" function that I wrote years ago, long before this half-precision project. But now the MATLAB object system insures that every individual arithmetic operation is done on unpacked fp16 numbers.

Let's generate a 5-by-5 matrix with random two-digit integer entries.

A = fp16(randi(100,5,5)) A = 82 10 16 15 66 91 28 98 43 4 13 55 96 92 85 92 96 49 80 94 64 97 81 96 68

I am going to use fp16 backslash to invert A. So I need the identity matrix in half precision.

I = fp16(eye(5)) I = 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1

Now the overloaded backslash calls lutx to compute the inverse.

X = A\I X = 0.0374 -0.0113 -0.0213 -0.0521 0.0631 -0.1044 0.0450 0.0364 0.1674 -0.1782 -0.0791 0.0458 0.0428 0.1270 -0.1550 0.1725 -0.0880 -0.0789 -0.2952 0.3445 -0.0355 0.0161 0.0285 0.0757 -0.0921

Compute the residual.

AX = A*X R = I - AX AX = 1.0000 -0.0001 0.0004 -0.0017 0.0009 0.0008 0.9995 0.0005 -0.0052 0.0043 0.0004 -0.0017 1.0010 -0.0076 -0.0009 0.0005 -0.0023 0.0007 0.9927 0.0013 0.0007 -0.0024 0.0009 -0.0084 1.0020 R = 0 0.0001 -0.0004 0.0017 -0.0009 -0.0008 0.0005 -0.0005 0.0052 -0.0043 -0.0004 0.0017 -0.0010 0.0076 0.0009 -0.0005 0.0023 -0.0007 0.0073 -0.0013 -0.0007 0.0024 -0.0009 0.0084 -0.0020

Both AX and R are what I expect from arithmetic that is accurate to only about three decimal digits.

Although I get a different random A every time I publish this blog post, I expect that it has a modest condition number.

kappa = cond(A) kappa = 211.1246

Since A is not badly conditioned, I can invert the computed inverse and expect to get close to the original integer matrix.

Z = X\I Z = 82.0625 10.0625 15.9844 15.0234 66.0625 91.1875 28.0625 97.8750 42.9688 4.1094 13.8750 55.8125 96.5000 92.6875 85.6875 92.7500 96.5625 49.4063 80.5000 94.5000 64.8125 97.6875 81.4375 96.5625 68.5625 fp16 SVD

I have just nonchalantly computed cond(A). But cond isn't on the list of overload methods for fp16. I was pleasantly surprised to find that matlab\matfun\cond.m quietly worked on this new datatype. Here is the core of that code.

dbtype cond 34:43, dbtype cond 47 34 if p == 2 35 s = svd(A); 36 if any(s == 0) % Handle singular matrix 37 c = Inf(class(A)); 38 else 39 c = max(s)./min(s); 40 if isempty(c) 41 c = zeros(class(A)); 42 end 43 end 47 end

So it is correctly using the singular value decomposition, and I have svd overloaded. The SVD computation is handled by a 433 line M-file, svdtx, that, like lutx, was written before fp16 existed.

Let's compute the SVD again.

[U,S,V] = svd(A) U = -0.2491 -0.5625 0.4087 0.5747 0.3521 -0.3560 -0.5132 -0.7603 -0.0077 -0.1742 -0.4626 0.6050 -0.1619 0.6060 -0.1621 -0.5474 -0.1196 0.4761 -0.3303 -0.5918 -0.5449 0.1981 -0.0316 -0.4402 0.6846 S = 333.5000 0 0 0 0 0 94.1875 0 0 0 0 0 84.0000 0 0 0 0 0 48.2500 0 0 0 0 0 1.5801 V = -0.4319 -0.8838 0.0479 -0.0894 0.1471 -0.4299 0.2233 0.1981 -0.7349 -0.4314 -0.4626 0.0955 -0.7461 0.3069 -0.3550 -0.4729 0.3684 -0.0754 -0.0961 0.7910 -0.4370 0.1544 0.6294 0.5903 -0.2014

Reconstruct A from its half precision SVD. It's not too shabby.

USVT = U*S*V' USVT = 81.9375 10.0703 16.0625 14.9453 66.0000 90.9375 27.9688 97.9375 42.9688 4.0391 12.9688 54.9688 96.0000 91.9375 84.9375 92.0000 96.0000 48.9688 79.9375 94.0000 63.9375 96.9375 80.9375 95.9375 67.8750

Finally, verify that we've been working all this time with fp16 objects.

whos Name Size Bytes Class Attributes A 5x5 226 fp16 AX 5x5 226 fp16 B 4x4 1576 string I 5x5 226 fp16 M 4x4 208 fp16 Me 4x1 184 fp16 R 5x5 226 fp16 S 5x5 226 fp16 U 5x5 226 fp16 USVT 5x5 226 fp16 V 5x5 226 fp16 X 5x5 226 fp16 Z 5x5 226 fp16 e 4x1 184 fp16 kappa 1x1 8 double Calculator

I introduced a calculator in my blog post about Roman numerals. Version 3.1 of Cleve's Laboratory also includes a fancier version of the calculator that computes in four different precisions -- quarter, half, single, and double -- and displays the results in four different formats -- decimal, hexadecimal, binary, and Roman.

I like to demonstrate the calculator by clicking on the keys

1 0 0 0 / 8 1 =

because the decimal expansion is a repeating .123456790.

Thanks

Thanks to MathWorkers Ben Tordoff, Steve Eddins, and Kiran Kintali who provided background and pointers to work on half precision.

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Get the MATLAB code (requires JavaScript)

Published with MATLAB® R2017a

. % Hardware support for half precision is now available on many processors, % including the GPU in the % . % Here is a link to an extensive article about half precision on the % . %% Floating point anatomy % The format of a floating point number is characterized by two % parameters, |p|, the number of bits in the fraction and |q|, the number % of bits in the exponent. I will consider four precisions, % _quarter_, _half_, _single_, and _double_. The quarter-precision % format is something that I just invented for this blog post; % it is not standard and actually not very useful. %% % The four pairs of characterizing parameters are p = [4, 10, 23, 52]; %% q = [3, 5, 8, 11]; %% % With these values of |p| and |q|, and with one more bit for the sign, % the total number of bits in the word, |w|, is a power of two. w = p + q + 1 %% % *Normalized numbers* % % Most floating point numbers are _normalized_, and are expressed as % % $$x = \pm (1+f)2^e$$ %% % The _fraction_ $f$ is in the half open interval % % $$0 \leq f < 1$$ % %% % The binary representation of $f$ requires at most |p| bits. % In other words $2^p f$ is an integer in the range % % $$0 \leq 2^p f < 2^p$$ %% % % The _exponent_ $e$ is an integer in the range % % $$-b+1 \leq e \leq b$$ % %% % The quantity $b$ is both the largest exponent and the |bias|. % % $$b = 2^{q-1} - 1$$ b = 2.^(q-1)-1 %% % The fractional part of a normalized number is $1+f$, but only $f$ % needs to be stored. That leading $1$ is known as the _hidden bit_. %% % *Subnormal* % % There are two values of the exponent $e$ for which the biased exponent, % $e+b$, reaches the smallest and largest values possible to represent % in |q| bits. The smallest is % % $$e + b = 0$$ % % The corresponding floating point numbers do not have a hidden leading % bit. These are the _subnormal_ or _denormal_ numbers. % % $$x = \pm f 2^{-b}$$ % %% % *Infinity and Not-A-Number* % % The largest possible biased exponent is % % $$e + b = 2^q-1$$. % % Quantities with this exponent field represent _infinities_ and % _NaN_, or _Not-A-Number_. %% % The percentage of floating point numbers that are exceptional % because they are subnormal, infinity or NaN increases as the % precision decreases. Exceptional exponents are only $2$ values % out of $2^q$. For double precision this is $2/2^{11}$, which is % less than a tenth of a percent, but for half precision it is $2/2^5$, % which is more than 6 percent. And fully one-fourth of all my toy % quarter precision floating point numbers are exceptional. %% % Encode the sign bit with |s = 0| for nonnegative and |s = 1| for % negative. And encode the exponent with an offsetting bias, |b|. % Then a floating point number can be packed in |w| bits with % % x = [s e+b 2^p*f] %% Precision and range % *epsilon* % % If a real number cannot be expressed with a binary expansion requiring % at most |p| bits, it must be approximated by a floating point number % that does have such a binary representation. This is _roundoff error_. % The important quantity characterizing precision is _machine epsilon_, % or |eps|. In MATLAB, |eps(x)| is the distance from |x| to the % next larger (in absolute value) floating point number. With no % argument, |eps| is simply the difference between |1| and the next larger % floating point number. format shortg eps = 2.^(-p) %% % This tells us that double precision is good for about 16 decimal digits % of accuracy, single for about 7 decimal digits, half for about 3, and % quarter for barely more than one. %% % *realmax* % % If a real number, or the result of an arithmetic operation, is too % large to be represented, it _overflows_ and is replaced by the % largest floating point number. This is realmax = 2.^b.*(2-eps) %% % *realmin* % % _Underflow_ and representation of very small numbers is more complicated. % The smallest normalized floating point number is realmin = 2.^(-b+1) %% % *tiny* % % But there are numbers smaller than |realmin|. IEEE 754 introduced the % notion of _gradual underflow_ and _denormal_ numbers. In the 2008 % revised standard their name was changed to _subnormal_. %% % Think of roundoff in numbers near underflow. Before 754 floating % point numbers had the disconcerting property that |x| and |y| could % be unequal, but their difference could underflow so |x-y| becomes |0|. % With 754 the gap between |0| and |realmin| is filled with numbers whose % spacing is the same as the spacing between |realmin| and |2*realmin|. % I like to call this spacing, and the smallest subnormal number, |tiny|. tiny = realmin.*eps %% Floating point integers % *flintmax* % % It is possible to do integer arithmetic with floating point numbers. % I like to call such numbers _flints_. When we write the numbers $3$ % and $3.0$, they are different descriptions of the same integer, but % we think of one as fixed point and the other as floating point. % The largest flint is |flintmax|. flintmax = 2./eps %% % Technically all the floating point numbers larger than |flintmax| % are integers, but the spacing between them is larger than one, so it % is not safe to use them for integer arithmetic. Only integer-valued % floating point numbers between |0| and |flintmax| are allowed to be % called flints. %% Table % Let's collect all these anatomical characteristics together in a % new MATLAB |table|. T = [w; p; q; b; eps; realmax; realmin; tiny; flintmax]; T = table(T(:,1), T(:,2), T(:,3), T(:,4), ... 'variablenames',{'quarter','half','single','double'}, ... 'rownames',{'w','p','q','b','eps','realmax','realmin', ... 'tiny','flintmax'}); disp(T) %% fp8 and fp16 % Version 3.1 of % includes code for objects [email protected]| and [email protected]| % that begin to provide full implementations of quarter-precision and % half-precision arithmetic. %% % The methods currently provided are methods(fp16) %% % These provide only partial implementations because the arithmetic is not % done on the compact forms. We cheat. For each individual scalar % operation, the operands are unpacked from their short storage into % old fashioned doubles. The operation is then carried out by existing % double precision code and the results returned to the shorter formats. % This simulates the reduced precision and restricted range, but requires % relatively little new code. %% % All of the work is done in the constructors [email protected]/fp8.m| and % [email protected]/fp16.m| and what we might call the "deconstructors" % [email protected]/double.m| and [email protected]/double.m|. % The constructors convert ordinary floating point % numbers to reduced precision representations by packing as many of % the 32 or 64 bits as will fit into 8 or 16 bit words. The deconstructors % do the reverse by unpacking things. %% % Once these methods are available, almost everything else is trivial. % The code for most operations is like this one for the overloaded % addition. type @fp16/plus.m %% Wikipedia test suite % The about half-precision includes several 16-bit examples % with the sign, exponent, and fraction fields separated. % I've added a couple more. % % 0 01111 0000000000 = 1 % 0 00101 0000000000 = 2^-10 = eps % 0 01111 0000000001 = 1+eps = 1.0009765625 (next smallest float after 1) % 1 10000 0000000000 = -2 % 0 11110 1111111111 = 65504 (max half precision) = 2^15*(2-eps) % 0 00001 0000000000 = 2^-14 = r ~ 6.10352e-5 (minimum positive normal) % 0 00000 1111111111 = r*(1-eps) ~ 6.09756e-5 (maximum subnormal) % 0 00000 0000000001 = r*eps ~ 5.96046e-8 (minimum positive subnormal) % 0 00000 0000000000 ~ r*eps/2 (underflow to zero) % 0 00000 0000000000 = 0 % 1 00000 0000000000 = -0 % 0 11111 0000000000 = infinity % 1 11111 0000000000 = -infinity % 0 11111 1111111111 = NaN % 0 01101 0101010101 = 0.333251953125 ~ 1/3 %% % This provides my test suite for checking |fp16| operations on scalars. clear zero = fp16(0); one = fp16(1); eps = eps(one); r = realmin(one); tests = {'1','eps','1+eps','-2','2/r*(2-eps)', ... 'r','r*(1-eps)','r*eps','r*eps/2', ... 'zero','-zero','1/zero','-1/zero','zero/zero','1/3'}; %% % Let's run the tests. for t = tests(:)' x = eval(t{:}); y = fp16(x); z = binary(y); w = double(y); fprintf(' %18s %04s %19.10g %19.10g %s\n', ... z,hex(y),double(x),w,t{:}) end %% Matrix operations % Most of the methods in [email protected]| and [email protected]| handle matrices. % The 4-by-4 magic square from Durer's Melancholia II provides my first % example. clear format short M = fp16(magic(4)) %% % Let's see how the packed 16-bit elements look in binary. B = binary(M) %% % Check that the row sums are all equal. This matrix-vector multiply % can be done exactly with the flints in the magic square. e = fp16(ones(4,1)) Me = M*e %% fp16 backslash % I've overloaded |mldivide|, so I can solve linear systems and % compute inverses. The actual computation is done by |lutx|, % a "textbook" function that I wrote years ago, long before this % half-precision project. But now the MATLAB object system insures % that every individual arithmetic operation is done on unpacked % |fp16| numbers. %% % Let's generate a 5-by-5 matrix with random two-digit integer entries. A = fp16(randi(100,5,5)) %% % I am going to use |fp16| backslash to invert |A|. % So I need the identity matrix in half precision. I = fp16(eye(5)) %% % Now the overloaded backslash calls |lutx| to compute the inverse. X = A\I %% % Compute the residual. AX = A*X R = I - AX %% % Both |AX| and |R| are what I expect from arithmetic that is % accurate to only about three decimal digits. %% % Although I get a different random |A| every time I publish this blog % post, I expect that it has a modest condition number. kappa = cond(A) %% % Since |A| is not badly conditioned, I can invert the computed inverse % and expect to get close to the original integer matrix. Z = X\I %% fp16 SVD % I have just nonchalantly computed |cond(A)|. But |cond| isn't on the % list of overload methods for |fp16|. I was pleasantly surprised to % find that |matlab\matfun\cond.m| quietly worked on this new datatype. % Here is the core of that code. dbtype cond 34:43, dbtype cond 47 %% % So it is correctly using the singular value decomposition, and I have % |svd| overloaded. The SVD computation is handled by a 433 line M-file, % |svdtx|, that, like |lutx|, was written before |fp16| existed. %% % Let's compute the SVD again. [U,S,V] = svd(A) %% % Reconstruct |A| from its half precision SVD. It's not too shabby. USVT = U*S*V' %% % Finally, verify that we've been working all this time % with |fp16| objects. whos %% Calculator % I introduced a |calculator| in my blog post about % . % Version 3.1 of % also includes a fancier version of the calculator % that computes in four different precisions REPLACE_WITH_DASH_DASH quarter, half, single, and % double REPLACE_WITH_DASH_DASH and displays the results in four different formats REPLACE_WITH_DASH_DASH decimal, % hexadecimal, binary, and Roman. %% % I like to demonstrate the calculator by clicking on the keys % % 1 0 0 0 / 8 1 = % % because the decimal expansion is a repeating |.123456790|. % % <> %% Thanks % Thanks to MathWorkers Ben Tordoff, Steve Eddins, and Kiran Kintali % who provided background and pointers to work on half precision. ##### SOURCE END ##### ff9f165871db4e949268c53e16278a8d -->

### Teaching Mechatronics with MATLAB, Simulink, and Arduino Hardware

latest technical articles - Wed, 05/03/2017 - 05:30
RPI’s mechatronics curriculum combines multidomain theory with lab projects based on a take-home kit that includes an Arduino based microcontroller, a DC motor, and MATLAB and Simulink software.

### Optimizing Automotive Manufacturing Processes with Discrete-Event Simulation

latest technical articles - Fri, 04/28/2017 - 05:30
Daimler engineers run simulations with Simulink and SimEvents to aid operational decision-making, forecast the outcomes of manufacturing process changes, and improve production lines efficiency.

### A Roman Numeral Object, with Arithmetic, Matrices and a Clock

latest Blogs - Mon, 04/24/2017 - 22:30

A MATLAB object for arithmetic with Roman numerals provides an example of object oriented programming. I had originally intended this as my April Fools post, but I got fascinated and decided to make it the subject of a legitimate article.

ContentsBackground

I've always been interested in Roman numerals. In my former life as a professor, when I taught the beginning computer programming course, one of my projects involved Roman numerals.

Doing arithmetic with Roman numerals is tricky. What is IV + VI ? Stop reading this blog for a moment and compute this sum.

Did you find that IV + VI = X ? How did you do that? You probably converted IV and VI to decimal, did the addition using decimal arithmetic, then converted the result back to Roman. You computed 4 + 6 = 10. That is also how my roman object works. I have no idea how the Romans did it without decimal arithmetic to rely on.

Roman Numerals

Recall that Roman numerals are formed from seven letters

I, V, X, L, C, D, and M.

Their values, expressed in decimal, are

1, 5, 10, 50, 100, 500, and 1000.

A Roman numeral is just a string of these letters, usually in decreasing order, like MMXVII. The value of the string is the sum of the values of the individual letters, that is 1000+1000+10+5+1+1, which is this year, 2017. But sometimes the letters are out of order. If one letter is followed by another with higher value, then the value of the first letter is subtracted, rather than added, to the sum. Two years from now will be MMXIX, which is 1000+1000+10-1+10 = 2019.

Extending Roman Numerals

I decided to jazz things up a bit by extending roman numerals to negative and fractional values. So I allow for a unary minus sign at the beginning of the string, and I allow lower case letters for fractions. The value of a lower case letter is the value of the corresponding upper case letter divided by 1000. Here are a few examples.

vi = 6/1000 -c = -100/1000 = -0.1 mmxvii = 2017/1000 = 2.017

These extentions introduce some aspects of floating point arithmetic to the system. Upper case letters evaluate to integers, equally spaced with an increment of one. Lower case letters evaluate to fractional values less than one (if you leave off 'm'), with an increment of 1/1000.

Evaluating Input Strings

Here is a function that accepts any string, looks for the fourteen letters, and sums their positive or negative values.

type roman_eval_string function n = roman_eval_string(s) % Convert a string to the .n component of a Roman numeral. D = 'IVXLCDM'; v = [1 5 10 50 100 500 1000]; D = [D lower(D)]; v = [v v/1000]; n = 0; t = 0; for d = s k = find(d == D); if ~isempty(k) u = v(k); if t < u n = n - t; else n = n + t; end t = u; end end n = n + t; if ~isempty(s) && s(1)=='-' n = -n; end end

Fractional values were obtained by adding just these two lines on code.

D = [D lower(D)]; v = [v v/1000];

Negative values come from the sign test at the end of the function.

Let's try it.

n = roman_eval_string('MMXIX') n = 2019

This will evaluate any string. No attempt is made to check for "correct" strings.

n = roman_eval_string('DDDCDVIVIIIIIVIIIIC') n = 2019

The subtraction rule is not always used. Clocks with Roman numerals for the hours sometimes denote 4 o'clock by IIII and sometimes by IV. So representations are not unique and correctness is elusive.

four = roman_eval_string('IIII') four = roman_eval_string('IV') four = 4 four = 4 Roman Object

Objects were introduced with MATLAB 5 in 1996. My first example of a MATLAB object was this roman object. I now have a directory @roman on my path. It includes all of the functions that define methods for the roman object. First and foremost is the constructor.

type @roman/roman function r = roman(a) %ROMAN Roman numeral class constructor. % r = ROMAN(a) converts a number or a string to a Roman numeral. % % A roman object retains its double precision numeric value. % The string representation of classic Roman numerals use just upper case % letters. Our "floating point" numerals use both upper and lower case. % % I 1 i 1/1000 = .001 % V 5 v 5/1000 = .002 % X 10 x 10/1000 = .01 % L 50 l 50/1000 = .05 % C 100 c 100/1000 = .1 % D 500 d 500/1000 = .5 % M 1000 m 1000/1000 = 1 % % The value of a string is the sum of the values of its letters, % except a letter followed by one of higher value is subtracted. % % Values >= decimal 4000 are represented by 'MMMM'. % Decimal 0 is represented by blanks. % % Blog: http://blogs.mathworks.com/cleve/2017/04/24. % See also: calculator. if nargin == 0 r.n = []; r = class(r,'roman'); return elseif isa(a,'roman') r = a; return elseif isa(a,'char') a = roman_eval_string(a); end r.n = a; r = class(r,'roman'); end % roman

If the input a is already a roman object, the constructor just returns it. If a is a string, such as 'MMXIX', the constructor calls roman_eval_string to convert a to a number n.

Finally the constuctor creates a roman object r containing a in its only field, the numeric value r.n. Consequently, we see that a roman object is just an ordinary double precision floating point number masquerading in this Homeric garb.

For example

r = roman(2019) r = 'MMXIX' Producing Character Output

Why did roman(2019) print MMXIX in that last example? That's because the object system calls upon @roman/display, which in turn calls @roman/char, to produce the output printed in the command window. Here is the crucial function @roman/char that converts the numerical field to its Roman representation.

type @roman/char function sea = char(r) % char Generate Roman representation of Roman numeral. % c = CHAR(r) converts an @roman number or matrix to a % cell array of character strings. rn = r.n; [p,q] = size(rn); sea = cell(p,q); for k = 1:p for j = 1:q if isempty(rn(k,j)) c = ''; elseif isinf(rn(k,j)) || rn(k,j) >= 4000 c = 'MMMM'; else % Integer part n = fix(abs(rn(k,j))); f = abs(rn(k,j)) - n; c = roman_flint2rom(n); % Fractional part, thousandths. if f > 0 fc = roman_flint2rom(round(1000*f)); c = [c lower(fc)]; end % Adjust sign if rn(k,j) < 0 c = ['-' c]; end end sea{k,j} = c; end end end % roman/char

The heavy lifting is done by this function which generates the character representation of an integer.

type roman_flint2rom function c = roman_flint2rom(x) D = {'','I','II','III','IV','V','VI','VII','VIII','IX' '','X','XX','XXX','XL','L','LX','LXX','LXXX','XC' '','C','CC','CCC','CD','D','DC','DCC','DCCC','CM' '','M','MM','MMM',' ',' ',' ',' ',' ',' '}; n = max(fix(x),0); i = 1; c = ''; while n > 0 c = [D{i,rem(n,10)+1} c]; n = fix(n/10); i = i + 1; end end

The functions roman_eval_string and roman_flint2rom are essentially inverses of each other. One converts a string of letters to a number and the other converts a number to a string of letters.

Converting a string to a numeric value and then converting it back to a string enforces a canonical representation of the result. So a nonconventional Roman numeral gets rectified.

r = roman('MMXVIIII') r = 'MMXIX' Producing Numeric Output

The crucial intermediate quantity in the previous example was the numeric value 2019. That can be uncovered with a one-liner.

type @roman/double function n = double(r) %DOUBLE Convert Roman numeral to double. % n = double(r) is the numeric value of a Roman numeral. n = r.n; end % roman/double year = double(r) year = 2019 Roman Methods

Here are all the operations that I can currently do with the roman class. I've overloaded just a handful to provide a proof of concept.

methods(r) Methods for class roman: char display minus mrdivide plus disp double mldivide mtimes roman

Binary arithmetic operations on roman objects are easy. Make sure both operands are roman and then do the arithmetic on the numeric fields.

type @roman/plus function r = plus(p,q) p = roman(p); q = roman(q); r = roman(p.n + q.n); end % roman/plus

So this is why IV + VI = X is just 4 + 6 = 10 under the covers.

r = roman('IV') + roman('VI') r = 'X' Roman Matrices

Did you notice that the output method char will handle matrices? Let's try one. Magic squares have integer elements. Here is the 4-by-4 from Durer's Melancholia II.

M = roman(magic(4)) M = 'XVI' 'II' 'III' 'XIII' 'V' 'XI' 'X' 'VIII' 'IX' 'VII' 'VI' 'XII' 'IV' 'XIV' 'XV' 'I'

Check that its row sums are all the same.

e = roman(ones(4,1)) Me = M*e e = 'I' 'I' 'I' 'I' Me = 'XXXIV' 'XXXIV' 'XXXIV' 'XXXIV' Roman Backslash

I've overloaded mldivide, so I can solve linear systems and compute inverses. All the elements of the 4-by-4 inverse Hilbert matrix are integers, but some are larger than 4000, so I'll scale the matrix by a factor of 2.

X = invhilb(4)/2 A = roman(X) X = 8 -60 120 -70 -60 600 -1350 840 120 -1350 3240 -2100 -70 840 -2100 1400 A = 'VIII' '-LX' 'CXX' '-LXX' '-LX' 'DC' '-MCCCL' 'DCCCXL' 'CXX' '-MCCCL' 'MMMCCXL' '-MMC' '-LXX' 'DCCCXL' '-MMC' 'MCD'

Inverting and rescaling A should produce the Hilbert matrix itself, where all of the elements are familiar fractions. I'll need the identity matrix, suitably scaled.

I = roman(eye(4))/2 I = 'd' '' '' '' '' 'd' '' '' '' '' 'd' '' '' '' '' 'd'

Now I call employ backslash to compute the inverse. Do you recognize the familiar fractions?

H = A\I H = 'm' 'd' 'cccxxxiii' 'ccl' 'd' 'cccxxxiii' 'ccl' 'cc' 'cccxxxiii' 'ccl' 'cc' 'clxvii' 'ccl' 'cc' 'clxvii' 'cxliii'

Here is some homework: why is H(1,1) represented by 'm', when it should be 'I'?

Residual

Finally, check the residual. It's all zero -- to the nearest thousandths.

R = I - A*H R = '' '' '' '-' '' '' '' '' '' '' '' '' '' '-' '-' '' Calculator

Two gizmos that exhibit the roman object are included in Version 3.0 of Cleve's Laboratory. One is a calculator.

calculator(2017) Roman Clock

The clock captures the date and time whenever I publish this blog.

roman_clock_snapshot

OK, let's quit foolin' around and get back to serious business.

\n'); d.write(code_string); // Add copyright line at the bottom if specified. if (copyright.length > 0) { d.writeln(''); d.writeln('%%'); if (copyright.length > 0) { d.writeln('% _' + copyright + '_'); } } d.write('\n'); d.title = title + ' (MATLAB code)'; d.close(); } -->

Get the MATLAB code (requires JavaScript)

Published with MATLAB® R2017a

. One is a calculator. calculator(2017) %% Roman Clock % The clock captures the date and time whenever I publish this blog. roman_clock_snapshot %% % OK, let's quit foolin' around and get back to serious business. ##### SOURCE END ##### e8271352388f47c4b4eae2c80f10f8f7 -->

### Bank Format and Metric Socket Wrenches

latest Blogs - Mon, 04/10/2017 - 22:30

A report about a possible bug in format bank and a visit to a local hardware store made me realize that doing decimal arithmetic with binary floating point numbers is like tightening a European bolt with an American socket wrench.

ContentsTax Time

It's mid-April and so those of us who file United States income taxes have that chore to do. Years ago, I used MATLAB to help with my taxes. I had a program named form1040.m that had one statement for each line on the tax form. I just had to enter my income and deductions. Then MATLAB would do all the arithmetic.

If we're really meticulous about our financial records, we keep track of things to the nearest penny. So line 28 of form1040 might have been something like

interest = 48.35 interest = 48.3500

I didn't like those trailing zeros in the output. So I introduced

format bank

into MATLAB. Now

interest interest = 48.35

is printed with just two digits after the decimal point.

format bank has turned out to be useful more broadly and is still in today's MATLAB.

Breaking Ties

We recently had a user ask about the rounding rule employed by format bank. What if a value fails halfway between two possible outputs? Which is chosen and why?

format short

so we can see four decimal places.

x = (5:10:45)'/1000 y = 1+x z = 2+x x = 0.0050 0.0150 0.0250 0.0350 0.0450 y = 1.0050 1.0150 1.0250 1.0350 1.0450 z = 2.0050 2.0150 2.0250 2.0350 2.0450

These values appear to fall halfway between pairs of two-digit decimal fractions. Let's see how the ties are broken.

format bank x y z x = 0.01 0.01 0.03 0.04 0.04 y = 1.00 1.01 1.02 1.03 1.04 z = 2.00 2.02 2.02 2.04 2.04

Look at the last digits. What mysterious hand is at work here? Three of the x's, x(1), x(3), and x(4), have been rounded up. None of the y's have been rounded up. Two of the z's, z(2) and z(4), have been rounded up.

Email circulated internally at MathWorks for a few days after this was reported suggesting various explanations. Is it a bug in format bank? In the I/O library? Does it depend upon which compiler was used to build MATLAB? Do we see the same behavior on the PC and the Mac? Has it always been this way? These are the usual questions that we ask ourselves when we see curious behavior.

Do you know what's happening?

None of the above.

Well, none of the suspects I just mentioned is the culprit. The fact is none of the numbers fall exactly on a midpoint. Think binary, not decimal. A value like 0.005 expressed as a decimal fraction cannot be represented exactly as a binary floating point number. Decimal fractions fall in between the binary fractions, and rarely fall precisely half way.

Symbolic Toolbox

To understand what is going on, the Symbolic Toolbox function

sym(x,'e')

is your friend. The 'e' flag is provided for this purpose. The help entry says

'e' stands for 'estimate error'. The 'r' form is supplemented by a term involving the variable 'eps' which estimates the difference between the theoretical rational expression and its actual floating point value. For example, sym(3*pi/4,'e') is 3*pi/4-103*eps/249.

To see how this works for the situation encountered here.

symx = sym(x,'e') symy = sym(y,'e') symz = sym(z,'e') symx = eps/2133 + 1/200 3/200 - eps/400 eps/160 + 1/40 (3*eps)/200 + 7/200 9/200 - (3*eps)/400 symy = 201/200 - (12*eps)/25 203/200 - (11*eps)/25 41/40 - (2*eps)/5 207/200 - (9*eps)/25 209/200 - (8*eps)/25 symz = 401/200 - (12*eps)/25 (14*eps)/25 + 403/200 81/40 - (2*eps)/5 (16*eps)/25 + 407/200 409/200 - (8*eps)/25

The output is not as clear as I would like to see it, but I can pick off the sign of the error terms and find

x + - + + - y - - - - - z - + - + -

Or, I can compute the signs with

format short sigx = sign(double(symx - x)) sigy = sign(double(symy - y)) sigz = sign(double(symz - z)) sigx = 1 -1 1 1 -1 sigy = -1 -1 -1 -1 -1 sigz = -1 1 -1 1 -1

The sign of the error term tells us whether the floating point numbers stored in x, y and z are larger or smaller than the anticipated decimal fractions. After this initial input, there is essentially no more roundoff error. format bank will round up or down from the decimal value accordingly. Again, it is just doing its job on the input it is given.

Socket Wrenches

Photo credit: http://toolguyd.com/

A high-end socket wrench set includes both metric (left) and fractional inch (right) sizes. Again, think decimal and binary. Metric sizes of nuts and bolts and wrenches are specified in decimal fractions, while the denominators in fractional inch sizes are powers of two.

Conversion charts between the metric and fractional inch standards abound on the internet. Here is a link to one of them: Wrench Conversion Chart.

But we can easily compute our own conversion chart. And in the process compute delta, the relative error made when the closest binary wrench is used on a decimal bolt.

make_chart Inch Metric delta 1/64 0.016 1/32 0.031 3/64 0.047 1mm 0.039 -0.191 1/16 0.063 5/64 0.078 2mm 0.079 0.008 3/32 0.094 7/64 0.109 1/8 0.125 3mm 0.118 -0.058 9/64 0.141 5/32 0.156 4mm 0.157 0.008 11/64 0.172 3/16 0.188 13/64 0.203 5mm 0.197 -0.032 7/32 0.219 15/64 0.234 6mm 0.236 0.008 1/4 0.250 9/32 0.281 7mm 0.276 -0.021 5/16 0.313 8mm 0.315 0.008 11/32 0.344 9mm 0.354 0.030 3/8 0.375 13/32 0.406 10mm 0.394 -0.032 7/16 0.438 15/32 0.469 12mm 0.472 0.008 1/2 0.500 9/16 0.563 14mm 0.551 -0.021 5/8 0.625 16mm 0.630 0.008 11/16 0.688 18mm 0.709 0.030 3/4 0.750 13/16 0.813 20mm 0.787 -0.032 7/8 0.875 22mm 0.866 -0.010 15/16 0.938 24mm 0.945 0.008 1 1.000

Let's plot those relative errors. Except for the small sizes, where this set doesn't have enough wrenches, the relative error is only a few percent. But that's still enough to produce a damaging fit on a tight nut.

bar(k,d) axis([0 25 -.05 .05]) xlabel('millimeters') ylabel('delta')

You might notice that my conversion chart, like all such charts, and like the wrenches themselves, has a little bit of floating point character. The spacing of the entries is not uniform. The spacing between the binary values is 1/64, then 1/32, then 1/16. The spacing of the metric values is 1mm at the top of the chart and 2mm later on.

10 millimeters

Suppose we want to tighten a 10mm nut and all we have are binary wrenches. The diameter of the nut in inches is

meter = 39.370079; d = 10*meter/1000 d = 0.3937

Consulting our chart, we see that a 13/32 wrench is the best fit, but it's a little too large. 10mm lies between these two binary values.

[floor(32*d) ceil(32*d)] b1 = 12/32; b2 = 13/32; [b1 d b2]' ans = 12 13 ans = 0.3750 0.3937 0.4063

The fraction of the interval is

f = (d - b1)/(b2 - b1) f = 0.5984

10mm is about 60% of the way from 12/32 inches to 13/32 inches.

One-tenth

Now let's turn to floating point numbers. What happens when execute this MATLAB statement?

x = 1/10 x = 0.1000

One is divided by ten and the closest floating point number is stored in x. The same value is produced by the statement

x = 0.1 x = 0.1000

The resulting x lies in the interval between 1/16 and 1/8. The floating point numbers in this interval are uniformly spaced with a separation of

e = eps(1/16) e = 1.3878e-17

This is 2^-56. Let

e = sym(e) e = 1/72057594037927936

This value e plays the role that 1/64 plays for my wrenches.

The result of x = 1/10 lies between these two binary fractions.

b1 = floor(1/(10*e))*e b2 = ceil(1/(10*e))*e b1 = 7205759403792793/72057594037927936 b2 = 3602879701896397/36028797018963968

The tiny interval of length e is

c = [b1 1/10 b2]' c = 7205759403792793/72057594037927936 1/10 3602879701896397/36028797018963968

In decimal

vpa(c) ans = 0.099999999999999991673327315311326 0.1 0.10000000000000000555111512312578

Where in this interval does 1/10 lie?

f = double((1/10 - b1)/e) f = 0.6000

So 1/10 is about 60% of the way from b1 to b2 and so is closer to b2 than to b1. The two statements

x = 1/10 x = double(b2) x = 0.1000 x = 0.1000

store exactly the same value in x.

The 13/32 wrench is the closest tool in the binary collection to the 10mm nut.

\n'); d.write(code_string); // Add copyright line at the bottom if specified. if (copyright.length > 0) { d.writeln(''); d.writeln('%%'); if (copyright.length > 0) { d.writeln('% _' + copyright + '_'); } } d.write('\n'); d.title = title + ' (MATLAB code)'; d.close(); } -->

Get the MATLAB code (requires JavaScript)

Published with MATLAB® R2017a

> % % Photo credit: http://toolguyd.com/ %% % A high-end socket wrench set includes both metric (left) and fractional % inch (right) sizes. Again, think decimal and binary. Metric sizes of % nuts and bolts and wrenches are specified in decimal fractions, while % the denominators in fractional inch sizes are powers of two. %% % Conversion charts between the metric and fractional inch standards % abound on the internet. Here is a link to one of them: % . %% % But we can easily compute our own conversion chart. And in the process % compute |delta|, the relative error made when the closest binary % wrench is used on a decimal bolt. make_chart %% % Let's plot those relative errors. Except for the small sizes, where % this set doesn't have enough wrenches, the relative error is only a few % percent. But that's still enough to produce a damaging fit on a tight % nut. bar(k,d) axis([0 25 -.05 .05]) xlabel('millimeters') ylabel('delta') %% % You might notice that my conversion chart, like all such charts, % and like the wrenches themselves, % has a little bit of floating point character. The spacing of the % entries is not uniform. The spacing between the binary values % is 1/64, then 1/32, then 1/16. The spacing of the metric values % is 1mm at the top of the chart and 2mm later on. %% 10 millimeters % Suppose we want to tighten a 10mm nut and all we have are binary % wrenches. The diameter of the nut in inches is meter = 39.370079; d = 10*meter/1000 %% % Consulting our chart, we see that a 13/32 wrench is the best fit, but % it's a little too large. 10mm lies between these two binary values. [floor(32*d) ceil(32*d)] b1 = 12/32; b2 = 13/32; [b1 d b2]' %% % The fraction of the interval is f = (d - b1)/(b2 - b1) %% % 10mm is about 60% of the way from 12/32 inches to 13/32 inches. %% One-tenth % Now let's turn to floating point numbers. % What happens when execute this MATLAB statement? x = 1/10 %% % One is divided by ten and the closest floating point number is % stored in |x|. The same value is produced by the statement x = 0.1 %% % The resulting |x| lies in the interval between 1/16 and 1/8. % The floating point numbers in this interval are uniformly spaced % with a separation of e = eps(1/16) %% % This is |2^-56|. Let e = sym(e) %% % This value |e| plays the role that 1/64 plays for my wrenches. %% % The result of |x = 1/10| lies between these two binary fractions. b1 = floor(1/(10*e))*e b2 = ceil(1/(10*e))*e %% % The tiny interval of length |e| is c = [b1 1/10 b2]' %% % In decimal vpa(c) %% % Where in this interval does 1/10 lie? f = double((1/10 - b1)/e) %% % So 1/10 is about 60% of the way from |b1| to |b2| and so is closer % to |b2| than to |b1|. The two statements x = 1/10 x = double(b2) %% % store exactly the same value in |x|. %% % The 13/32 wrench is the closest tool in the binary collection to % the 10mm nut. ##### SOURCE END ##### e7a69dc0fbce44ddbf028f28e4a1a71b -->

### 7 Financial Risks Modeled in MATLAB

latest technical articles - Wed, 04/05/2017 - 05:30
Learn how you can use MATLAB to model all quantifiable risks and service multiple compliance regimes with agility, reproducibility, and robust model governance.

### My Erdös Number and My Trump Number

latest Blogs - Sat, 04/01/2017 - 22:30

I've long known that my Erdös Number is 3. This means that the length of the path on the graph of academic coauthorship between me and mathematician Paul Erdös is 3. Somewhat to my surprise, I recently discovered that I can also trace a chain of coauthorship to Donald J. Trump. My Trump number is 5.

Contents

Paul Erdös. Photo: http://www.iitvidya.com/paul-erdos.

Collaborative Distance

The collaborative distance between two authors is the length of the path of coauthorship of scientific papers, books, and articles connecting the two. If A and B are coauthors, then the collaborative distance between them is 1. Furthermore, if B and C are also coauthors, then the collaborative distance between A and C is 2. And so on. If there is no chain of coauthorship, then the collaborative distance is infinite.

Erdös Number

Paul Erdös (1911-1996) was the world's most prolific modern mathematician. He wrote 1,523 papers with 511 distinct coauthors. These 511 people have Erdös number equal to 1. And these authors have, in turn, written papers with over 11,000 other people. That means that over 11,000 people have Erdös number of 2. My estimate is that a few hundred thousand people have Erdös number of 3. I'm one of them.

There are two length three paths of coauthorship between me and Erdös. Both go through my thesis advisor, George Forsythe. I wrote a blog post about Forsythe a few years ago. Forsythe has an Erdös number of 2 in two different ways because he wrote papers with his thesis advisor, William Feller, and with Ernst Straus, both of whom had worked directly with Erdös.

An Erdös Number calculator is available at MathSciNet Collaboration Distance. More than you every wanted to know about Erdös numbers is available at the Oakland University Erdös Number Project.

Steve Johnson

Steve Johnson is a buddy of mine who also has Erdös number of 3. His path goes through Jeff Ullman and Ron Graham to Paul Erdös. But that's not why I bring him up today. In the 1970's Steve was part of the group at Bell Labs that developed Unix. He wrote the Unix tool Yacc (Yet Another Compiler Compiler), as well as the original C compiler, PCC, (Portable C Compiler). He is coauthor, along with three other Unix guys, of a paper about C in the 1978 issue of the Bell System Technical Journal that was devoted entirely to Unix.

Steve and I wrote a paper about compiling MATLAB, although the references to that paper on the Internet have my named spelled incorrectly. It is through this paper that I was surprised to find that my collaborative distance from Donald J. Trump is only 5.

Trump Number

Finite Trump numbers are possible because Trump's famous book, "The Art of the Deal", was actually ghost-written by a free-lance writer named Tony Schwartz. And Schwartz has coauthored many articles and books with other people. Some of these articles might not exactly be classified as academic papers, but what the heck.

John Winslow Morgan

The coauthorship path between me and Trump is through articles by John Winslow Morgan, a professor in the Harvard Business School who specializes in the history of technology. He has written articles with Schwarz and with Dennis Ritchie, one of the originators of Unix.

So, the path of length 5 is Moler - Johnson - Ritchie - Morgan - Schwartz - Trump.

References

Erdös Number

Forsythe, G. E. and Straus, E. G. On best conditioned matrices. Proc. Amer. Math. Soc. 6, (1955). 340–345.

Erdös, P., Lovász, L., Simmons, A., and Straus, E. G. Dissection graphs of planar point sets. A survey of combinatorial theory. (Proc. Internat. Sympos., Colorado State Univ., Fort Collins, Colo., 1971), 139–149. North-Holland, Amsterdam, 1973.

Feller, William, and George E. Forsythe. New matrix transformations for obtaining characteristic vectors. Quarterly of Applied Mathematics 8.4 (1951), 325-331.

Erdös, Paul, William Feller, and Harry Pollard. A property of power series with positive coefficients. Bull. Amer. Math. Soc 55.2 (1949): 201-204.

Forsythe, G. E. and C. B. Moler, Computer Solution of Linear Algebraic Systems, (Series in Automatic Computation) XI + 148, Prentice Hall, Englewood Cliffs, N.J. 1967.

Forsythe, George E., Malcolm, Michael A. and Moler, Cleve B., Computer Methods for Mathematical Computations, (Series in Automatic Computation) XI + 259, Prentice Hall, Englewood Cliffs, N.J. 1977.

Trump Number

Johnson, S. C. and C. Mohler (Moler), Compiling MATLAB, Proceedings of the USENIX Symposium on Very High Level Languages (VHLL), 119-27, Santa Fe, New Mexico, October 1994. USENIX Association.

Ritchie, D. M., Johnson, S. C., Lesk, M. E. and Kernighan, B. W., UNIX Time-Sharing System: The C Programming Language. Bell System Technical Journal, 57: 1991–2019, 1978.

Ritchie, D. M. and Morgan, J. W, The Origins of UNIX, Harvard Business Review, 48: 28-35, 1985.

Morgan, John Winslow and Schwartz, Tony, Does UNIX Have A Future?, MIT Technology Review, 53: 1-8, 1992.

Trump, Donald J. and Schwartz, Tony, The Art of the Deal, Ballantine Books, (paperback), 384 pp., 2004.

\n'); d.write(code_string); // Add copyright line at the bottom if specified. if (copyright.length > 0) { d.writeln(''); d.writeln('%%'); if (copyright.length > 0) { d.writeln('% _' + copyright + '_'); } } d.write('\n'); d.title = title + ' (MATLAB code)'; d.close(); } -->

Get the MATLAB code (requires JavaScript)

Published with MATLAB® R2017a

> % % Paul ErdÃ¶s. Photo: http://www.iitvidya.com/paul-erdos. %% Collaborative Distance % The _collaborative distance_ between two authors is the length of % the path of coauthorship of scientific papers, books, and articles % connecting the two. If A and B are coauthors, then the % collaborative distance between them is 1. Furthermore, if B and C % are also coauthors, then the collaborative distance between A and C % is 2. And so on. If there is no chain of coauthorship, then the % collaborative distance is infinite. %% ErdÃ¶s Number % Paul ErdÃ¶s (1911-1996) was the world's most prolific modern % mathematician. He wrote 1,523 papers with 511 distinct coauthors. % These 511 people have ErdÃ¶s number equal to 1. % And these authors have, in turn, written papers with over % 11,000 other people. That means that over 11,000 people % have ErdÃ¶s number of 2. % My estimate is that a few hundred thousand people have ErdÃ¶s number % of 3. I'm one of them. %% % There are two length three paths of coauthorship between me and % ErdÃ¶s. Both go through my thesis advisor, George Forsythe. % I wrote a % a few years ago. % Forsythe has an ErdÃ¶s number of 2 in two different ways because he % wrote papers with his thesis advisor, William Feller, and with % Ernst Straus, both of whom had worked directly with ErdÃ¶s. %% % An ErdÃ¶s Number calculator is available at % . % More than you every wanted to know about ErdÃ¶s numbers % is available at the Oakland University % . %% Steve Johnson % Steve Johnson is a buddy of mine who also has ErdÃ¶s number of 3. % His path goes through Jeff Ullman and Ron Graham to Paul ErdÃ¶s. % But that's not why I bring him up today. In the 1970's Steve % was part of the group at Bell Labs that developed Unix. % He wrote the Unix tool Yacc (Yet Another Compiler Compiler), % as well as the original C compiler, PCC, (Portable C Compiler). % He is coauthor, along with three other Unix guys, of a paper about % C in the 1978 issue of the Bell System Technical Journal that was % devoted entirely to Unix. %% % Steve and I wrote a paper about compiling MATLAB, although the % references to that paper on the Internet have my named spelled % incorrectly. It is through this paper that I was surprised to % find that my collaborative distance from Donald J. Trump is only 5. %% Trump Number % Finite Trump numbers are possible because Trump's famous book, % "The Art of the Deal", was actually ghost-written by a free-lance % writer named Tony Schwartz. And Schwartz has coauthored many % articles and books with other people. Some of these articles % might not exactly be classified as academic papers, but what the heck. %% John Winslow Morgan % The coauthorship path between me and Trump is through articles % by John Winslow Morgan, a professor in the Harvard Business School % who specializes in the history of technology. He has written % articles with Schwarz and with Dennis Ritchie, one of the originators % of Unix. %% % So, the path of length 5 is Moler - Johnson - Ritchie - Morgan - % Schwartz - Trump. %% References % *ErdÃ¶s Number* %% % Forsythe, G. E. and Straus, E. G. % _On best conditioned matrices._ % Proc. Amer. Math. Soc. 6, (1955). 340â€“345. %% % ErdÃ¶s, P., LovÃ¡sz, L., Simmons, A., and Straus, E. G. % _Dissection graphs of planar point sets._ % A survey of combinatorial theory. % (Proc. Internat. Sympos., Colorado State Univ., Fort Collins, Colo., 1971), % 139â€“149. North-Holland, Amsterdam, 1973. %% % Feller, William, and George E. Forsythe. % _New matrix transformations for obtaining characteristic vectors_. % Quarterly of Applied Mathematics 8.4 (1951), 325-331. %% % ErdÃ¶s, Paul, William Feller, and Harry Pollard. % _A property of power series with positive coefficients._ % Bull. Amer. Math. Soc 55.2 (1949): 201-204. %% % Forsythe, G. E. and C. B. Moler, % Computer Solution of Linear Algebraic Systems, % (Series in Automatic Computation) XI + 148, % Prentice Hall, Englewood Cliffs, N.J. 1967. %% % Forsythe, George E., Malcolm, Michael A. and Moler, Cleve B., % Computer Methods for Mathematical Computations, % (Series in Automatic Computation) XI + 259, % Prentice Hall, Englewood Cliffs, N.J. 1977. %% % *Trump Number* %% % Johnson, S. C. and C. Mohler (Moler), % _Compiling MATLAB_, % Proceedings of the USENIX Symposium on Very High Level Languages (VHLL), % 119-27, Santa Fe, New Mexico, October 1994. % USENIX Association. %% % Ritchie, D. M., Johnson, S. C., Lesk, M. E. and Kernighan, B. W., % _UNIX Time-Sharing System: The C Programming Language._ % Bell System Technical Journal, 57: 1991â€“2019, 1978. %% % Ritchie, D. M. and Morgan, J. W, % _The Origins of UNIX_, % Harvard Business Review, 48: 28-35, 1985. %% % Morgan, John Winslow and Schwartz, Tony, % _Does UNIX Have A Future?_, % MIT Technology Review, 53: 1-8, 1992. %% % Trump, Donald J. and Schwartz, Tony, % The Art of the Deal, % Ballantine Books, (paperback), 384 pp., 2004. ##### SOURCE END ##### 98707fe7d7bb49c0bdfc15f995e75654 -->

### Online-Schätzung für Echtzeit-Fehlererkennung in einem Gleichstrommotor

latest technical articles - Tue, 03/28/2017 - 05:30
In diesem Artikel geht es darum, wie ein Online-Anlagenmodell für einen Gleichstrommotor entwickelt und dazu eingesetzt wird, Änderungen der Motordynamik in Echtzeit zu erfassen.

### Designing a Torque Controller for a PMSM through Simulation on a Virtual Dynamometer

latest technical articles - Tue, 03/21/2017 - 05:30
This article introduces a workflow that reduces hardware testing time by using simulation to design and test a torque controller for an FEA-based high-fidelity PMSM machine.

### Morse Code, Binary Trees and Graphs

latest Blogs - Mon, 03/20/2017 - 22:30

A binary tree is an elegant way to represent and process Morse code. The new MATLAB graph object provides an elegant way to manipulate binary trees. A new app, morse_tree, based on this approach, is now available in version 2.40 of Cleve's Laboratory.

ContentsEXM chapter

I have a chapter on Morse Code and binary trees in Experiments with MATLAB. Some of this blog post is taken from that chapter. But today's implentation is much more, as I said, elegant. In EXM I represented a binary tree as a cell array of cell arrays. Now I am using clever indexing into a linear character string, together with the MATLAB graph object. The resulting code is much shorter and more readable.

Binary trees

Here's all we have to do to get a picture of a binary tree. First we create the adjacency matrix and view its spy plot.

n = 31; j = 2:n; k = floor(j/2); A = sparse(k,j,1,n,n); spy(A)

Then we create and plot the directed graph. The MATLAB digraph object, and its supporting plot method, are doing all of the work.

G = digraph(A); Gp = plot(G); set(gca,'xtick',[],'ytick',[])

This is the complete binary tree with five levels, and consequently $2^5-1$ nodes. (In defiance of nature, computer scientists put the root of a tree at the top.)

Follow the nodes in numerical order, from top to bottom and left to right. You are doing a breadth first traversal of the structure. The successors of the node with index j have indices 2j and 2j+1. This makes old-fashioned linear indexing possible.

I want to save the coordinates of the nodes for use in the layout in my next plot.

x = Gp.XData; y = Gp.YData; Morse code

Morse code is no longer important commercially, but it still has some avid fans among hobbyists. Morse code was invented over 150 years ago, not by Samuel F. B. Morse, but by his colleague, Alfred Vail. It has been in widespread use ever since. The code consists of short dots, '.', and longer dashes, '-', separated by short and long spaces. You may be familiar with the international distress signal, '... --- ...', the code for "SOS", abbreviating "Save Our Ships" or perhaps "Save Our Souls". But did you notice that some modern cell phones signal '... -- ...', the code for "SMS", indicating activity of the "Short Message Service".

Until 2003, a license to operate an amateur radio in the US required minimal proficiency in Morse code. When I was in junior high school, I learned Morse code to get my ham license, and I've never forgotten it.

According to Wikipedia, in 2004, the International Telecommunication Union formally added a code for the ubiquitous email character, @, to the international Morse code standard. This was the first addition since World War I.

Morse tree

I could provide a table showing that '.-' is the code for A, '-...' the code for B, and so on. But I'm not going to do that, and my MATLAB program does not have such a table. As far as I am concerned, Morse code is defined by a MATLAB statement containing the 26 upper case letters of the English language, an asterisk, and four blanks.

morse = '*ETIANMSURWDKGOHVF L PJBXCYZQ ';

The asterisk at the start serves as the root of the tree. The four blanks will correspond to four missing nodes in the bottom level of the tree. So the binary tree is not going to be complete. The automatic layout in the graph/plot method does not draw a perfect tree. This is why I saved the node coordinates from the plot of the full tree. Let's remove the rows and columns of these potentially unlabeled nodes from the adjacency matrix, and the coordinates.

m = find(morse == ' '); A(:,m) = []; A(m,:) = []; x(m) = []; y(m) = [];

Convert the character array into a cell array of individual chars.

nodes = num2cell(morse); nodes(m) = '';

Create the directed graph with these node labels.

G = digraph(A,nodes);

Plot the graph, using the layout saved from the full tree.

plot(G,'xdata',x,'ydata',y, ... 'showarrows','off'); set(gca,'xtick',[],'ytick',[])

If you follow the links downward and emit a dot when you go left and a dash when you go right, you have Morse code. For example start at the root, go left once, then right once. You will reach A and will have generated '.-', the code for A.

Morse code is already present in the defining character vector morse, even before we create and plot a graph. The character with index 5 is A. The characters with indices 2*5 and 2*5+1 are R and W.

j = 5; morse(j) morse(2*j:2*j+1) ans = 'A' ans = 'RW'

Consequently, the Morse code for R and W is '.-.' and '.--'. This works everywhere in the first half of morse. The characters in the second half don't (yet) have successors.

Extensions

Morse code can be extended by replacing the blanks in the basic tree with characters that are not in the 26 letter English alphabet and adding two more levels to the tree. Several different extensions have been in use at different times and different regions of the world. The Wikipedia page for Morse code includes a binary tree with some commonly used extensions. The fifth level includes the 10 decimal digits, several non-Latin chacters, and a few punctuation marks. The sixth level includes several punctuation marks.

morse_tree app

Here is a screen shot from the new app, morse_tree, that is available in version 2.40 of Cleve's Laboratory. Clicking on the toggle labeled "extend" shows two more levels of the tree and some extensions, although not many as Wikipedia's tree. Clicking on a node in our tree highlights that node while the sound of the corresponding code is played. This screen shot shows A highlighted.

Text entered in the box below the tree will be encoded and the sound played.

The speed of the generated code is set by the control currently labeled "5 wpm", for five words per minute.

morse_tree_extended

. %% EXM chapter % I have a % and binary trees in _Experiments with MATLAB_. % Some of this blog post is taken from that chapter. But today's % implentation is much more, as I said, elegant. In _EXM_ I represented % a binary tree as a cell array of cell arrays. Now I am using clever % indexing into a linear character string, together with the MATLAB graph % object. The resulting code is much shorter and more readable. %% Binary trees % Here's all we have to do to get a picture of a binary tree. % First we create the adjacency matrix and view its spy plot. n = 31; j = 2:n; k = floor(j/2); A = sparse(k,j,1,n,n); spy(A) %% % Then we create and plot the directed graph. The MATLAB |digraph| % object, and its supporting |plot| method, are doing all of the work. G = digraph(A); Gp = plot(G); set(gca,'xtick',[],'ytick',[]) %% % This is the complete binary tree with five levels, % and consequently $2^5-1$ nodes. % (In defiance of nature, computer scientists put the _root_ of a tree % at the top.) %% % Follow the nodes in numerical order, from top to bottom and left % to right. You are doing a _breadth first traversal_ of the structure. % The _successors_ of the node with index _j_ have indices _2j_ and _2j+1_. % This makes old-fashioned linear indexing possible. %% % I want to save the coordinates of the nodes for use in the layout in % my next plot. x = Gp.XData; y = Gp.YData; %% Morse code % Morse code is no longer important commercially, but it still has some % avid fans among hobbyists. % Morse code was invented over 150 years ago, not by Samuel F. B. Morse, % but by his colleague, Alfred Vail. It has been in widespread use ever % since. The code consists of short _dots_, '.', and longer _dashes_, % '-', separated by short and long spaces. % You may be familiar with the international distress signal, % '... REPLACE_WITH_DASH_DASH- ...', the code for "SOS", abbreviating "Save Our Ships" % or perhaps "Save Our Souls". But did you notice that some modern cell % phones signal '... REPLACE_WITH_DASH_DASH ...', the code for "SMS", indicating activity % of the "Short Message Service". %% % Until 2003, a license to operate an amateur radio in the US required % minimal proficiency in Morse code. When I was in junior high school, I % learned Morse code to get my ham license, and I've never forgotten it. %% % According to Wikipedia, in 2004, the International Telecommunication % Union formally added a code for the ubiquitous email character, @, % to the international Morse code standard. This was the first addition % since World War I. %% Morse tree % I could provide a table showing that '.-' is the code for A, % '-...' the code for B, and so on. But I'm not going to do that, % and my MATLAB program does not have such a table. As far as I am % concerned, Morse code is _defined_ by a MATLAB statement containing % the 26 upper case letters of the English language, an asterisk, and % four blanks. morse = '*ETIANMSURWDKGOHVF L PJBXCYZQ '; %% % The asterisk at the start serves as the root of the tree. % The four blanks will correspond to four missing nodes in the bottom % level of the tree. So the binary tree is not going to be complete. % The automatic layout in the |graph/plot| method does not draw a % perfect tree. This is why I saved the node coordinates from the % plot of the full tree. % Let's remove the rows and columns of these potentially unlabeled % nodes from the adjacency matrix, and the coordinates. m = find(morse == ' '); A(:,m) = []; A(m,:) = []; x(m) = []; y(m) = []; %% % Convert the character array into a cell array of individual chars. nodes = num2cell(morse); nodes(m) = ''; %% % Create the directed graph with these node labels. G = digraph(A,nodes); %% % Plot the graph, using the layout saved from the full tree. plot(G,'xdata',x,'ydata',y, ... 'showarrows','off'); set(gca,'xtick',[],'ytick',[]) %% % If you follow the links downward and emit a _dot_ when you go left % and a _dash_ when you go right, you have Morse code. For example % start at the root, go left once, then right once. You will reach A % and will have generated '.-', the code for A. %% % Morse code is already present in the defining character vector |morse|, % even before we create and plot a graph. % The character with index |5| is |A|. The characters with indices % |2*5| and |2*5+1| are |R| and |W|. j = 5; morse(j) morse(2*j:2*j+1) %% % Consequently, the Morse code for R and W is '.-.' and '.REPLACE_WITH_DASH_DASH'. % This works everywhere in the first half of |morse|. % The characters in the second half don't (yet) have successors. %% Extensions % Morse code can be extended by replacing the blanks in the basic tree % with characters that are not in the 26 letter English alphabet and % adding two more levels to the tree. Several different extensions % have been in use at different times and different regions of the world. % The for % Morse code includes a binary tree with some commonly used extensions. % The fifth level includes the 10 decimal digits, several non-Latin % chacters, and a few punctuation marks. The sixth level includes % several punctuation marks. %% morse_tree app % Here is a screen shot from the new app, |morse_tree|, that is % available in version 2.40 of % . % Clicking on the toggle labeled "extend" shows two more levels of the % tree and some extensions, although not many as Wikipedia's tree. % Clicking on a node in our tree highlights that node while the sound of % the corresponding code is played. This screen shot shows A highlighted. %% % Text entered in the box below the tree will be encoded and the sound % played. %% % The speed of the generated code is set by the control % currently labeled "5 wpm", for five words per minute. morse_tree_extended %% % Here's a snap quiz: What is Morse code for the @ sign? % Download |morse_tree| to check your answer. ##### SOURCE END ##### 947b15a050fb4f9d902c4b56fe419943 -->