jutil.py 文件源码

def svm_SVR_C( xM, yV, c_l, graph = True):
    """
    SVR is performed iteratively with different C values
    until all C in the list are used.
    """

    r2_l, sd_l = [], []
    for C in c_l:
        print('sklearn.svm.SVR(C={})'.format( C))
        clf = svm.SVR( C = C)
        clf.fit( xM, yV.A1)
        yV_pred = clf.predict(xM)       

        r2, sd = regress_show( yV, np.mat( yV_pred).T, graph = graph)
        for X, x in [[r2_l, r2], [sd_l, sd]]:
            X.append( x)

    print('average r2, sd are', np.mean( r2_l), np.mean( sd_l))


    if graph:
        pdw = pd.DataFrame( { 'log10(C)': np.log10(c_l), 'r2': r2_l, 'sd': sd_l})
        pdw.plot( x = 'log10(C)')

    return r2_l, sd_l