pca.py 文件源码

def demoPCA():
    n = 1000
    t = np.linspace(0.0, 30*np.pi, n)

    s1 = spsig.sawtooth(t)
    s2 = np.cos(0.5*t)
    #s3 = np.random.normal(scale=1.2, size=t.size)
    s3 = np.random.uniform(-2.0, 2.0, size=t.size)
    s = np.vstack((s1,s2,s3)).T

    theta1 = np.pi/6.0
    rot1 = np.array([[np.cos(theta1), -np.sin(theta1), 0.0],
                     [np.sin(theta1),  np.cos(theta1), 0.0],
                     [0.0,            0.0,           1.0]])

    theta2 = np.pi/4.0
    rot2 = np.array([[ np.cos(theta2), 0.0, np.sin(theta2)],
                     [ 0.0,            1.0, 0.0],
                     [-np.sin(theta2), 0.0, np.cos(theta2)]])

    theta3 = np.pi/5.0
    rot3 = np.array([[1.0, 0.0,             0.0],
                     [0.0, np.cos(theta3), -np.sin(theta3)],
                     [0.0, np.sin(theta3),  np.cos(theta3)]])

    sMixed = s.dot(rot1).dot(rot2).dot(rot3)

    lags = 0
    pcaFilt = PCA(sMixed, lags=lags)

    ##pcaFilt.plotMags()

    fig = plt.figure()

    axOrig = fig.add_subplot(4,1, 1)
    axOrig.plot(s+util.colsep(s))
    axOrig.set_title('Unmixed Signal')
    axOrig.autoscale(tight=True)

    axMixed = fig.add_subplot(4,1, 2)
    axMixed.plot(sMixed+util.colsep(sMixed))
    axMixed.set_title('Mixed Signal (3d rotation)')
    axMixed.autoscale(tight=True)

    axUnmixed = fig.add_subplot(4,1, 3)
    pcaFilt.plotTransform(sMixed, ax=axUnmixed)
    axUnmixed.set_title('PCA Components')
    axUnmixed.autoscale(tight=True)

    axCleaned = fig.add_subplot(4,1, 4)
    pcaFilt.plotFilter(sMixed, comp=(1,2,), ax=axCleaned)
    axCleaned.set_title('Cleaned Signal (First Component Removed)')
    axCleaned.autoscale(tight=True)

    fig.tight_layout()