gather

Gather the properties of a linear regression model fitted with GPU array data.

Load the carsmall data set. Create X as a numeric matrix that contains three car performance metrics. Create Y as a numeric vector that contains the corresponding miles per gallon.

load carsmall
X = [Weight,Horsepower,Acceleration];
Y = MPG;

Convert the predictor X and response Y to gpuArray (Parallel Computing Toolbox) objects.

X = gpuArray(X);
Y = gpuArray(Y);

Fit a linear regression model mdl by using fitlm.

mdl = fitlm(X,Y);

Display the coefficients of mdl and determine whether the estimated coefficient values are GPU arrays.

mdl.Coefficients

ans=4×4 table
                    Estimate        SE          tStat        pValue  
                   __________    _________    _________    __________

    (Intercept)        47.977       3.8785        12.37    4.8957e-21
    x1             -0.0065416    0.0011274      -5.8023    9.8742e-08
    x2              -0.042943     0.024313      -1.7663       0.08078
    x3              -0.011583      0.19333    -0.059913       0.95236

isgpuarray(mdl.Coefficients.Estimate)

ans = logical
   1

Gather the properties of the linear regression model.

gatheredMdl = gather(mdl);

Display the coefficients of gatheredMdl and determine whether the estimated coefficient values are GPU arrays.

gatheredMdl.Coefficients

ans=4×4 table
                    Estimate        SE          tStat        pValue  
                   __________    _________    _________    __________

    (Intercept)        47.977       3.8785        12.37    4.8957e-21
    x1             -0.0065416    0.0011274      -5.8023    9.8742e-08
    x2              -0.042943     0.024313      -1.7663       0.08078
    x3              -0.011583      0.19333    -0.059913       0.95236

isgpuarray(gatheredMdl.Coefficients.Estimate)

ans = logical
   0

Gather the properties of a linear regression model and a k-nearest neighbor classifier. Both models are fitted using GPU array data.

Load the carsmall data set. Create X as a numeric matrix that contains three car performance metrics, and convert the predictor X to a gpuArray object.

load carsmall
X = [Weight,Horsepower,Acceleration];
X = gpuArray(X);

Fit a linear regression model of MPG (miles per gallon) as a function of the predictor X.

mdlLinear = fitlm(X,MPG);

Train a 3-nearest neighbor classifier using the predictor X and the classes Cylinders. Standardize the noncategorical predictor data.

mdlKNN = fitcknn(X,Cylinders,'NumNeighbors',3,'Standardize',1);

Gather the properties of the mdLinear and mdlKNN models.

[gMdlLinear,gMdlKNN] = gather(mdlLinear,mdlKNN);

Determine whether the p-value of the Durbin-Watson test for the regression model mdlLinear is a GPU array.

isgpuarray(dwtest(mdlLinear))

ans = logical
   1

Determine whether the p-value of the Durbin-Watson test for the gathered regression model gMdlLinear is a GPU array.

isgpuarray(dwtest(gMdlLinear))

ans = logical
   0

Determine whether the resubstitution loss of the classifier mdlKNN is a GPU array.

isgpuarray(resubLoss(mdlKNN))

ans = logical
   1

Determine whether the resubstitution loss of the gathered classifier gMdlKNN is a GPU array.

isgpuarray(resubLoss(gMdlKNN))

ans = logical
   1