EvaluationTools.py 文件源码

def applyNNBigRandom(Xin,model,reps=10,msize=500,start=150):
    #Returns an adjacency matrix
    n_features=Xin.shape[1]

    X=Xin.copy()
    #Center
    X -= X.mean(axis=0)
    std = X.std(axis=0)
    std[std == 0] = 1
    X /= std
    C_Final=np.zeros((X.shape[1],X.shape[1]))
    ind=np.arange(0,X.shape[1])
    larger=np.zeros((msize,msize))
    for i in xrange(reps):      
        np.random.shuffle(ind)
        I=np.eye(X.shape[1])
        P=I[:,ind]
        larger[start:start+n_features,start:start+n_features]=P.T.dot(X.T.dot(X)).dot(P)/X.shape[0]
        emp_cov_matrix=np.expand_dims(larger,0)
        pred=model.predict(np.expand_dims(emp_cov_matrix,0))
        pred=pred.reshape(msize,msize)[start:start+n_features,start:start+n_features]
        C=np.zeros((X.shape[1],X.shape[1]))
        C[np.triu_indices(n_features,k=1)]=pred[np.triu_indices(n_features,k=1)]
        C=C+C.T
        C=P.dot(C).dot(P.T)
        C_Final+=C
    C_Final=C_Final/float(reps)
    return C_Final