principal_component_analysis.py 文件源码

def main():

    # Demo of how to reduce the dimensionality of the data to two dimension
    # and plot the results. 

    # Load the dataset
    data = datasets.load_digits()
    X = data.data
    y = data.target

    # Project the data onto the 2 primary principal components
    X_trans = PCA().transform(X, 2)

    x1 = X_trans[:, 0]
    x2 = X_trans[:, 1]

    cmap = plt.get_cmap('viridis')
    colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(y)))]

    class_distr = []
    # Plot the different class distributions
    for i, l in enumerate(np.unique(y)):
        _x1 = x1[y == l]
        _x2 = x2[y == l]
        _y = y[y == l]
        class_distr.append(plt.scatter(_x1, _x2, color=colors[i]))

    # Add a legend
    plt.legend(class_distr, y, loc=1)

    # Axis labels
    plt.suptitle("PCA Dimensionality Reduction")
    plt.title("Digit Dataset")
    plt.xlabel('Principal Component 1')
    plt.ylabel('Principal Component 2')
    plt.show()