gather

Gather properties of Statistics and Machine Learning Toolbox object from GPU

Since R2020b

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    Syntax

    gatheredObj = gather(obj)
    [gatheredObj1,gatheredObj2,...,gatheredObjn] = gather(obj1,obj2,...,objn)

    Description

    gatheredObj = gather(obj) gathers all properties of the input object obj and returns the gathered object gatheredObj. All properties of the output object are stored in the local workspace.

    Use gather to create a Statistics and Machine Learning Toolbox™ object with properties stored in the local workspace from an object fitted using data stored as a GPU array. For more details on GPU arrays, see gpuArray (Parallel Computing Toolbox). Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox).

    example

    [gatheredObj1,gatheredObj2,...,gatheredObjn] = gather(obj1,obj2,...,objn) gathers the properties of multiple objects obj1,obj2,...,objn and returns the corresponding gathered objects gatheredObj1,gatheredObj2,...,gatheredObjn. The number of input arguments and output arguments must match.

    example

    Examples

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    This example uses:

    Open Live Script

    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

    This example uses:

    Open Live Script

    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

    Input Arguments

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    Object fitted with GPU arrays or a gpuArray object, specified as a regression model object, classification model object, probability distribution object, cvpartition object, or gpuArray (Parallel Computing Toolbox) object. A gpuArray object represents an array stored on the GPU.

    For more information on the Statistics and Machine Learning Toolbox objects supported by gather, see Supported Regression Models, Supported Classification Models, and Supported Probability Distribution Objects.

    More About

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    Tips

    • Gathering GPU arrays can be costly and is generally not necessary unless you need to use the results with functions that do not support GPU arrays. For the complete list of Statistics and Machine Learning Toolbox functions that accept GPU arrays, see Function List (GPU Arrays).

    • You can also call gather on other data types, such as distributed, codistributed, or tall arrays. If the data type does not support gathering, then gather has no effect.

    Extended Capabilities

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    Version History

    Introduced in R2020b

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    See Also

    (Parallel Computing Toolbox) | (Parallel Computing Toolbox) | | | | | | | | | | | | | | |

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