train.py 文件源码

def trainRBM_SVM(features, Cparam, nComponents):
    [X, Y] = listOfFeatures2Matrix(features)
    rbm = BernoulliRBM(n_components = nComponents, n_iter = 30, learning_rate = 0.2,  verbose = True)
    rbm.fit(X,Y)
    newX = rbm.transform(X)
#    colors = ["r","g","b"]
#    for i in range(1,Y.shape[0],5):
#        plt.plot(newX[i,:], colors[int(Y[i])])
#    plt.show()

    classifier = {}
    classifier["rbm"] = rbm    
    svm = sklearn.svm.SVC(C = Cparam, kernel = 'linear',  probability = True)        
    svm.fit(newX,Y)

    classifier["svm"] = svm

    return classifier