test.py

print(__doc__)
from time import time

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import offsetbox
from sklearn import (manifold, datasets, decomposition, ensemble,
                     discriminant_analysis, random_projection)

digits = datasets.load_digits(n_class=6)
X = digits.data
y = digits.target
n_samples, n_features = X.shape
n_neighbors = 30


#----------------------------------------------------------------------
# Scale and visualize the embedding vectors
def plot_embedding(X, title=None):
    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)

    plt.figure()
    ax = plt.subplot(111)
#    for i in range(X.shape[0]):
#        plt.text(X[i, 0], X[i, 1], str(digits.target[i]),
#                 color=plt.cm.Set1(y[i] / 10.),
#                 fontdict={'weight': 'bold', 'size': 9})

    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(digits.data.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)
            if np.min(dist) < 4e-3:
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),
                X[i])
            ax.add_artist(imagebox)
    plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title)


#----------------------------------------------------------------------
# Plot images of the digits
#n_img_per_row = 20
#img = np.zeros((10 * n_img_per_row, 10 * n_img_per_row))
#for i in range(n_img_per_row):
#    ix = 10 * i + 1
#    for j in range(n_img_per_row):
#        iy = 10 * j + 1
#        img[ix:ix + 8, iy:iy + 8] = X[i * n_img_per_row + j].reshape((8, 8))
#
#plt.imshow(img, cmap=plt.cm.binary)
#plt.xticks([])
#plt.yticks([])
#plt.title('A selection from the 64-dimensional digits dataset')



#----------------------------------------------------------------------
# t-SNE embedding of the digits dataset
print("Computing t-SNE embedding")
tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)
t0 = time()
X_tsne = tsne.fit_transform(X)

plt.scatter(X_tsne[:,0], X_tsne[:,1], s=10,alpha=0.5)
plt.show()