plotter.py

import matplotlib.pyplot as plt
import numpy as np

# import ipdb


def plotDataset(X, D):
    plt.clf()
    plt.plot(X[:, D[0]], X[:, D[1]], "bo")
    plt.xlabel("X" + str(D[0]))
    plt.ylabel("X" + str(D[1]))
    plt.show()
    plt.draw()


def plotClustering(X, M, A, D):
    """Plot a proclus clustering result.
    X: the data matrix
    M: medoid indices
    A: cluster assignments
    D: dimensions to plot
    """
    plt.clf()
    plt.xlabel("X" + str(D[0]))
    plt.ylabel("X" + str(D[1]))
    d1, d2 = D
    colors = np.empty(X.shape[0], dtype="object")
    colors[np.where(A == -1)[0]] = "0.7"  # gray for outliers
    picks = ["b", "g", "r", "c", "m", "k", "y"]

    print("cluster counts:")
    print(np.unique(A))
    print([len(np.where(A == i)[0]) for i in np.unique(A)])

    i = 0
    for c in np.setdiff1d(np.unique(A), [-1]):
        if i >= len(picks):
            raise Exception("used more colors than i have...")
        colors[np.where(A == c)[0]] = picks[i]
        i += 1

    plt.scatter(X[:, d1], X[:, d2], c=colors.tolist(), marker="o", s=40)
    # plot medoids as orange diamonds:
    plt.plot(X[M, d1], X[M, d2], marker="D", mfc="#FFFF4D", ms=7, ls="")
    plt.show()
    plt.draw()