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This example shows how to generate fixed-point C/C++ code for the prediction of a support vector machine (SVM) model. Compared to the general C/C++ code generation workflow, fixed-point code generation requires an additional step that defines the fixed-point data types of the variables required for prediction. Create a fixed-point data type structure by using `generateLearnerDataTypeFcn`

, and use the structure as an input argument of `loadLearnerForCoder`

in an entry-point function. You can also optimize the fixed-point data types before generating code.

This flow chart shows the fixed-point code generation workflow.

Train an SVM model.

Save the trained model by using

`saveLearnerForCoder`

.Define the fixed-point data types of the variables required for prediction by using the data type function generated by

`generateLearnerDataTypeFcn`

.Define an entry-point function that loads the model by using both

`loadLearnerForCoder`

and the structure, and then calls the`predict`

function.(Optional) Optimize the fixed-point data types.

Generate fixed-point C/C++ code.

Verify the generated code.

Step 5 is an optional step to improve the performance of the generated fixed-point code. To do so, repeat these two steps until you are satisfied with the code performance:

Record minimum and maximum values of the variables for prediction by using

`buildInstrumentedMex`

(Fixed-Point Designer).View the instrumentation results using

`showInstrumentationResults`

(Fixed-Point Designer). Then, tune the fixed-point data types (if necessary) to prevent overflow and underflow, and to improve the precision of the fixed-point code.

In this workflow, you define the fixed-point data types by using the data type function generated from `generateLearnerDataTypeFcn`

. Separating data types of the variables from the algorithm makes testing simpler. You can programmatically toggle data types between floating-point and fixed-point by using the input argument of the data type function. Also, this workflow is compatible with Manual Fixed-Point Conversion Workflow (Fixed-Point Designer).

Load the `census1994`

data set. This data set consists of demographic data from the US Census Bureau used to predict whether an individual makes over $50,000 a year.

`load census1994`

Consider a model that predicts the salary category of employees given their age, working class, education level, capital gain and loss, and number of working hours per week. Extract the variables of interest and save them using a table.

tbl = adultdata(:,{'age','education_num','capital_gain','capital_loss','hours_per_week'});

Print a summary of the table.

summary(tbl)

Variables: age: 32561x1 double Values: Min 17 Median 37 Max 90 education_num: 32561x1 double Values: Min 1 Median 10 Max 16 capital_gain: 32561x1 double Values: Min 0 Median 0 Max 99999 capital_loss: 32561x1 double Values: Min 0 Median 0 Max 4356 hours_per_week: 32561x1 double Values: Min 1 Median 40 Max 99

The scales of the variables are not consistent. In this case, you can train a model using a standardized data set by specifying the `'Standardize'`

name-value pair argument of `fitcsvm`

. However, adding the operations for standardization to the fixed-point code can reduce precision and increase memory use. Instead, you can manually standardize the data set, as shown in this example. The example also describes how to check the memory use at the end.

Fixed-point code generation does not support tables or categorical arrays. So, define the predictor data `X`

using a numeric matrix, and define the class labels `Y`

using a logical vector. A logical vector uses memory most efficiently in a binary classification problem.

```
X = table2array(tbl);
Y = adultdata.salary == '<=50K';
```

Define the observation weights `w`

.

w = adultdata.fnlwgt;

The memory use of a trained model increases as the number of support vectors in the model increases. To reduce the number of support vectors, you can increase the box constraint when training by using the `'BoxConstraint'`

name-value pair argument or use a subsampled representative data set for training. Note that increasing the box constraint can lead to longer training times, and using a subsampled data set can reduce the accuracy of the trained model. In this example, you randomly sample 1000 observations from the data set and use the subsampled data for training.

rng('default') % For reproducibility [X_sampled,idx] = datasample(X,1000,'Replace',false); Y_sampled = Y(idx); w_sampled = w(idx);

Find the weighted means and standard deviations by training the model using the `'Weight'`

and `'Standardize'`

name-value pair arguments.

tempMdl = fitcsvm(X_sampled,Y_sampled,'Weight',w_sampled,'KernelFunction','gaussian','Standardize',true); mu = tempMdl.Mu; sigma = tempMdl.Sigma;

If you do not use the `'Cost'`

, `'Prior'`

, or `'Weight'`

name-value pair argument for training, then you can find the mean and standard deviation values by using the `zscore`

function.

[standardizedX_sampled,mu,sigma] = zscore(X_sampled);

Standardize the predictor data by using `mu`

and `sigma`

.

standardizedX = (X-mu)./sigma; standardizedX_sampled = standardizedX(idx,:);

You can use a test data set to validate the trained model and to test an instrumented MEX function. Specify a test data set and standardize the test predictor data by using `mu`

and `sigma`

.

XTest = table2array(adulttest(:,{'age','education_num','capital_gain','capital_loss','hours_per_week'})); standardizedXTest = (XTest-mu)./sigma; YTest = adulttest.salary == '<=50K';

Train a binary SVM classification model.

Mdl = fitcsvm(standardizedX_sampled,Y_sampled,'Weight',w_sampled,'KernelFunction','gaussian');

`Mdl`

is a `ClassificationSVM`

model.

Compute the classification error for the training data set and the test data set.

loss(Mdl,standardizedX_sampled,Y_sampled)

ans = 0.1663

loss(Mdl,standardizedXTest,YTest)

ans = 0.1905

The SVM classifier misclassifies approximately 17% of the training data and 19% of the test data.

Save the SVM classification model to the file `myMdl.mat`

by using `saveLearnerForCoder`

.

`saveLearnerForCoder(Mdl,'myMdl');`

Use `generateLearnerDataTypeFcn`

to generate a function that defines the fixed-point data types of the variables required for prediction of the SVM model. Use all available predictor data to obtain realistic ranges for the fixed-point data types.

`generateLearnerDataTypeFcn('myMdl',[standardizedX; standardizedXTest])`

`generateLearnerDataTypeFcn`

generates the `myMdl_datatype`

function. Display the contents of `myMdl_datatype.m`

by using the `type`

function.

`type myMdl_datatype.m`

function T = myMdl_datatype(dt) %MYMDL_DATATYPE Define data types for fixed-point code generation % % T = MYMDL_DATATYPE(DT) returns the data type structure T, which defines % data types for the variables required to generate fixed-point C/C++ code % for prediction of a machine learning model. Each field of T contains a % fixed-point object returned by fi. The input argument dt specifies the % DataType property of the fixed-point object. Specify dt as 'Fixed' (default) % for fixed-point code generation or specify dt as 'Double' to simulate % floating-point behavior of the fixed-point code. % % Use the output structure T as both an input argument of an entry-point % function and the second input argument of loadLearnerForCoder within the % entry-point function. For more information, see loadLearnerForCoder. % File: myMdl_datatype.m % Statistics and Machine Learning Toolbox Version 12.1 (Release R2021a) % Generated by MATLAB, 23-Feb-2021 19:10:54 if nargin < 1 dt = 'Fixed'; end % Set fixed-point math settings fm = fimath('RoundingMethod','Floor', ... 'OverflowAction','Wrap', ... 'ProductMode','FullPrecision', ... 'MaxProductWordLength',128, ... 'SumMode','FullPrecision', ... 'MaxSumWordLength',128); % Data type for predictor data T.XDataType = fi([],true,16,11,fm,'DataType',dt); % Data type for output score T.ScoreDataType = fi([],true,16,14,fm,'DataType',dt); % Internal variables % Data type of the squared distance dist = (x-sv)^2 for the Gaussian kernel G(x,sv) = exp(-dist), % where x is the predictor data for an observation and sv is a support vector T.InnerProductDataType = fi([],true,16,6,fm,'DataType',dt); end

**Note:** If you click the button located in the upper-right section of this example and open the example in MATLAB®, then MATLAB opens the example folder. This folder includes the entry-point function file.

The `myMdl_datatype`

function uses the default word length (16) and proposes the maximum fraction length to avoid overflows, based on the default word length (16) and safety margin (10%) for each variable.

Create a structure `T`

that defines the fixed-point data types by using `myMdl_datatype`

.

`T = myMdl_datatype('Fixed')`

`T = `*struct with fields:*
XDataType: [0x0 embedded.fi]
ScoreDataType: [0x0 embedded.fi]
InnerProductDataType: [0x0 embedded.fi]

The structure `T`

includes the fields for the named and internal variables required to run the `predict`

function. Each field contains a fixed-point object, returned by `fi`

(Fixed-Point Designer). For example, display the fixed-point data type properties of the predictor data.

T.XDataType

ans = [] DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 16 FractionLength: 11 RoundingMethod: Floor OverflowAction: Wrap ProductMode: FullPrecision MaxProductWordLength: 128 SumMode: FullPrecision MaxSumWordLength: 128

For more details about the generated function and the structure, see Data Type Function.

Define an entry-point function named `myFixedPointPredict`

that does the following:

Accept the predictor data

`X`

and the fixed-point data type structure`T`

.Load a fixed-point version of a trained SVM classification model by using both

`loadLearnerForCoder`

and the structure T.Predict labels and scores using the loaded model.

function [label,score] = myFixedPointPredict(X,T) %#codegen Mdl = loadLearnerForCoder('myMdl','DataType',T); [label,score] = predict(Mdl,X); end

Optimize the fixed-point data types by using `buildInstrumentedMex`

and `showInstrumentationResults`

. Record minimum and maximum values of all named and internal variables for prediction by using `buildInstrumentedMex`

. View the instrumentation results using `showInstrumentationResults`

; then, based on the results, tune the fixed-point data type properties of the variables.

**Specify Input Argument Types of Entry-Point Function**

Specify the input argument types of `myFixedPointPredict`

using a 2-by-1 cell array.

ARGS = cell(2,1);

The first input argument is the predictor data. The `XDataType`

field of the structure `T`

specifies the fixed-point data type of the predictor data. Convert `X`

to the type specified in `T.XDataType`

by using the `cast`

(Fixed-Point Designer) function.

`X_fx = cast(standardizedX,'like',T.XDataType);`

The test data set does not have the same size as the training data set. Specify `ARGS{1}`

by using `coder.typeof`

(MATLAB Coder) so that the MEX function can take variable-size inputs.

ARGS{1} = coder.typeof(X_fx,size(standardizedX),[1,0]);

The second input argument is the structure `T`

, which must be a compile-time constant. Use `coder.Constant`

(MATLAB Coder) to specify `T`

as a constant during code generation.

ARGS{2} = coder.Constant(T);

**Create Instrumented MEX Function**

Create an instrumented MEX function by using `buildInstrumentedMex`

(Fixed-Point Designer).

Specify the input argument types of the entry-point function by using the

`-args`

option.Specify the MEX function name by using the

`-o`

option.Compute a histogram by using the

`-histogram`

option.Allow full code generation support by using the

`-coder`

option.

buildInstrumentedMex myFixedPointPredict -args ARGS -o myFixedPointPredict_instrumented -histogram -coder

Code generation successful.

**Test Instrumented MEX Function**

Run the instrumented MEX function to record instrumentation results.

[labels_fx1,scores_fx1] = myFixedPointPredict_instrumented(X_fx,T);

You can run the instrumented MEX function multiple times to record results from various test data sets. Run the instrumented MEX function using `standardizedXTest`

.

```
Xtest_fx = cast(standardizedXTest,'like',T.XDataType);
[labels_fx1_test,scores_fx1_test] = myFixedPointPredict_instrumented(Xtest_fx,T);
```

**View Results of Instrumented MEX Function**

Call `showInstrumentationResults`

(Fixed-Point Designer) to open a report containing the instrumentation results. View the simulation minimum and maximum values, proposed fraction length, percent of current range, and whole number status.

`showInstrumentationResults('myFixedPointPredict_instrumented')`

The proposed word lengths and fraction lengths in `X`

are the same as those in `XDataType`

in the structure `T`

.

View the histogram for a variable by clicking on the **Variables** tab.

The window contains the histogram and dialog panels with information about the variable. For information on this window, see the `NumericTypeScope`

(Fixed-Point Designer) reference page.

Clear the results by using `clearInstrumentationResults`

(Fixed-Point Designer).

`clearInstrumentationResults('myFixedPointPredict_instrumented')`

**Verify Instrumented MEX Function**

Compare the outputs from `predict`

and `myFixedPointPredict_instrumented`

.

[labels,scores] = predict(Mdl,standardizedX); verify_labels1 = isequal(labels,labels_fx1)

`verify_labels1 = `*logical*
0

`isequal`

returns logical 1 (true) if `labels`

and `labels_fx1`

are equal. If the labels are not equal, you can compute the percentage of incorrectly classified labels as follows.

diff_labels1 = sum(strcmp(string(labels_fx1),string(labels))==0)/length(labels_fx1)*100

diff_labels1 = 0.1228

Find the maximum of the relative differences between the score outputs.

diff_scores1 = max(abs((scores_fx1.double(:,1)-scores(:,1))./scores(:,1)))

diff_scores1 = 83.0713

**Tune Fixed-Point Data Types**

You can tune the fixed-point data types if the recorded results show overflow or underflow, or if you want to improve the precision of the generated code. Modify the fixed-point data types by updating the `myMdl_datatype`

function and creating a new structure, and then generate the code using the new structure. To update the `myMdl_datatype`

function, you can manually modify the fixed-point data types in the function file (`myMdl_datatype.m`

). Or, you can generate the function by using `generateLearnerDataTypeFcn`

and specifying a longer word length, as shown in this example. For more details, see Tips.

Generate a new data type function. Specify the word length 32 and the name `myMdl_datatype2`

for the generated function.

generateLearnerDataTypeFcn('myMdl',[standardizedX; standardizedXTest],'WordLength',32,'OutputFunctionName','myMdl_datatype2')

Display the contents of `myMdl_datatype2.m`

.

`type myMdl_datatype2.m`

function T = myMdl_datatype2(dt) %MYMDL_DATATYPE2 Define data types for fixed-point code generation % % T = MYMDL_DATATYPE2(DT) returns the data type structure T, which defines % data types for the variables required to generate fixed-point C/C++ code % for prediction of a machine learning model. Each field of T contains a % fixed-point object returned by fi. The input argument dt specifies the % DataType property of the fixed-point object. Specify dt as 'Fixed' (default) % for fixed-point code generation or specify dt as 'Double' to simulate % floating-point behavior of the fixed-point code. % % Use the output structure T as both an input argument of an entry-point % function and the second input argument of loadLearnerForCoder within the % entry-point function. For more information, see loadLearnerForCoder. % File: myMdl_datatype2.m % Statistics and Machine Learning Toolbox Version 12.1 (Release R2021a) % Generated by MATLAB, 23-Feb-2021 19:12:22 if nargin < 1 dt = 'Fixed'; end % Set fixed-point math settings fm = fimath('RoundingMethod','Floor', ... 'OverflowAction','Wrap', ... 'ProductMode','FullPrecision', ... 'MaxProductWordLength',128, ... 'SumMode','FullPrecision', ... 'MaxSumWordLength',128); % Data type for predictor data T.XDataType = fi([],true,32,27,fm,'DataType',dt); % Data type for output score T.ScoreDataType = fi([],true,32,30,fm,'DataType',dt); % Internal variables % Data type of the squared distance dist = (x-sv)^2 for the Gaussian kernel G(x,sv) = exp(-dist), % where x is the predictor data for an observation and sv is a support vector T.InnerProductDataType = fi([],true,32,22,fm,'DataType',dt); end

The `myMdl_datatype2`

function specifies the word length 32 and proposes the maximum fraction length to avoid overflows.

Create a structure `T2`

that defines the fixed-point data types by using `myMdl_datatype2`

.

`T2 = myMdl_datatype2('Fixed')`

`T2 = `*struct with fields:*
XDataType: [0x0 embedded.fi]
ScoreDataType: [0x0 embedded.fi]
InnerProductDataType: [0x0 embedded.fi]

Create a new instrumented MEX function, record the results, and view the results by using `buildInstrumentedMex`

and `showInstrumentationResults`

.

X_fx2 = cast(standardizedX,'like',T2.XDataType); buildInstrumentedMex myFixedPointPredict -args {X_fx2,coder.Constant(T2)} -o myFixedPointPredict_instrumented2 -histogram -coder

Code generation successful.

```
[labels_fx2,scores_fx2] = myFixedPointPredict_instrumented2(X_fx2,T2);
showInstrumentationResults('myFixedPointPredict_instrumented2')
```

Review the instrumentation report, and then clear the results.

`clearInstrumentationResults('myFixedPointPredict_instrumented2')`

Verify `myFixedPointPredict_instrumented2`

.

verify_labels2 = isequal(labels,labels_fx2)

`verify_labels2 = `*logical*
0

diff_labels2 = sum(strcmp(string(labels_fx2),string(labels))==0)/length(labels_fx2)*100

diff_labels2 = 0.0031

diff_scores2 = max(abs((scores_fx2.double(:,1)-scores(:,1))./scores(:,1)))

diff_scores2 = 2.0602

The percentage of incorrectly classified labels `diff_labels2`

and the relative difference in score values `diff_scores2`

are smaller than those from the previous MEX function generated using the default word length (16).

For more details about optimizing fixed-point data types by instrumenting MATLAB® code, see the reference pages `buildInstrumentedMex`

(Fixed-Point Designer), `showInstrumentationResults`

(Fixed-Point Designer), and `clearInstrumentationResults`

(Fixed-Point Designer), and the example Set Data Types Using Min/Max Instrumentation (Fixed-Point Designer).

Generate code for the entry-point function using `codegen`

. Instead of specifying a variable-size input for a predictor data set, specify a fixed-size input by using `coder.typeof`

. If you know the size of the predictor data set that you pass to the generated code, then generating code for a fixed-size input is preferable for the simplicity of the code.

codegen myFixedPointPredict -args {coder.typeof(X_fx2,[1,5],[0,0]),coder.Constant(T2)}

Code generation successful.

`codegen`

generates the MEX function `myFixedPointPredict_mex`

with a platform-dependent extension.

You can verify the `myFixedPointPredict_mex`

function in the same way that you verify the instrumented MEX function. See the **Verify Instrumented MEX Function** section for details.

[labels_sampled,scores_sampled] = predict(Mdl,standardizedX_sampled); n = size(standardizedX_sampled,1); labels_fx = true(n,1); scores_fx = zeros(n,2); for i = 1:n [labels_fx(i),scores_fx(i,:)] = myFixedPointPredict_mex(X_fx2(idx(i),:),T2); end verify_labels = isequal(labels_sampled,labels_fx)

`verify_labels = `*logical*
1

diff_labels = sum(strcmp(string(labels_fx),string(labels_sampled))==0)/length(labels_fx)*100

diff_labels = 0

diff_scores = max(abs((scores_fx(:,1)-scores_sampled(:,1))./scores_sampled(:,1)))

diff_scores = 0.0638

A good practice is to manually standardize predictor data before training a model. If you use the `'Standardize'`

name-value pair argument instead, then the generated fixed-point code includes standardization operations, which can cause loss of precision and increased memory use.

If you generate a static library, you can find the memory use of the generated code by using a code generation report. Specify `-config:lib`

to generate a static library, and use the `-report`

option to generate a code generation report.

codegen myFixedPointPredict -args {coder.typeof(X_fx2,[1,5],[0,0]),coder.Constant(T2)} -o myFixedPointPredict_lib -config:lib -report

On the **Summary** tab of the code generation report, click **Code Metrics**. The Function Information section shows the accumulated stack size.

To find the memory use of a model trained with `'Standardized','true'`

, you can run the following code.

Mdl = fitcsvm(X_sampled,Y_sampled,'Weight',w_sampled,'KernelFunction','gaussian','Standardize',true); saveLearnerForCoder(Mdl,'myMdl'); generateLearnerDataTypeFcn('myMdl',[X; XTest],'WordLength',32,'OutputFunctionName','myMdl_standardize_datatype') T3 = myMdl_standardize_datatype('Fixed'); X_fx3 = cast(X_sampled,'like',T3.XDataType); codegen myFixedPointPredict -args {coder.typeof(X_fx3,[1,5],[0,0]),coder.Constant(T3)} -o myFixedPointPredict_standardize_lib -config:lib -report

`generateLearnerDataTypeFcn`

| `loadLearnerForCoder`

| `saveLearnerForCoder`

| `buildInstrumentedMex`

(Fixed-Point Designer) | `cast`

(Fixed-Point Designer) | `clearInstrumentationResults`

(Fixed-Point Designer) | `fi`

(Fixed-Point Designer) | `showInstrumentationResults`

(Fixed-Point Designer) | `codegen`

(MATLAB Coder)

- Fixed-Point Data Types (Fixed-Point Designer)
- Create Fixed-Point Data in MATLAB (Fixed-Point Designer)
- Set Data Types Using Min/Max Instrumentation (Fixed-Point Designer)