def draw(self):
import matplotlib.pyplot as plt
plt, fig, ax = self.draw_chlcv(plt)
if self.window != None:
plt, fig, ax = self.draw_move_average_line(plt, fig, ax)
if self.lines != []:
plt, fig, ax = draw_line(plt, fig, ax, self.lines)
if self.small_peak_info != None:
plt, fig, ax = draw_peak(plt, fig, ax, self.small_peak_info,
"small")
if self.large_peak_info != None:
plt, fig, ax = draw_peak(plt, fig, ax, self.large_peak_info,
"large")
if self.vertical_lines != []:
plt, fig, ax = draw_vertical_line(plt, fig, ax,
self.vertical_lines)
ax.legend(fontsize=12)
plt.title("{}_{}".format(self.method_name, self.code))
save_path = self.save(plt)
plt.close(fig)
return save_path
def plot_progress_k_means(X, history_centroids, idx, K, i):
"""
Helper function that displays the progress of k-Means as it is running. It is intended for use only with 2D data.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Samples, where n_samples is the number of samples and n_features is the number of features.
history_centroids : ndarray, shape (n_max_iters, K, n_features)
The history of centroids assignment.
idx : ndarray, shape (n_samples, 1)
Centroid assignments.
K : int
The number of centroids.
i : int
Current iteration count.
"""
plot_data_points(X, idx, K)
plt.plot(history_centroids[0:i+1, :, 0], history_centroids[0:i+1, :, 1],
linestyle='', marker='x', markersize=10, linewidth=3, color='k')
plt.title('Iteration number {}'.format(i + 1))
for centroid_idx in range(history_centroids.shape[1]):
for iter_idx in range(i):
draw_line(history_centroids[iter_idx, centroid_idx, :], history_centroids[iter_idx + 1, centroid_idx, :])
def test_draw_line(self):
screen = "\0" * 10
draw_line(screen, 16, 8, 15, 2)
print ' '.join(format(ord(byte), 'b') for byte in screen)
plt.ylim(2, 8)
plt.gca().set_aspect('equal', adjustable='box')
plt.show()
# =============== Part 2: Principal Component Analysis ===============
print 'Running PCA on example dataset.'
# Before running PCA, it is important to first normalize X
X_norm, mu, sigma = feature_normalize(X)
# Run PCA
U, S, V = pca(X_norm)
plt.figure()
draw_line(mu, mu + 1.5 * S[0] * U[:,0].T)
draw_line(mu, mu + 1.5 * S[1] * U[:,1].T)
plt.show()
print 'Top eigenvector:'
print 'U = ', U[:, 0]
print '(you should expect to see -0.70710678 -0.70710678)'
# =================== Part 3: Dimension Reduction ===================
print 'Dimension reduction on example dataset.'
# Plot the normalized dataset (returned from pca)
plt.figure()
plt.scatter(X_norm[:, 0], X_norm[:, 1], facecolors='none', edgecolors='b')
plt.xlim(-4, 3)
from sina.exercise import *
from draw_home_line import draw_home_line
from draw_home_pie1 import draw_pie1
from draw_home_pie2 import draw_pie2
from draw_home_pie3 import draw_pie3
from draw_home_pie4 import draw_pie4
from draw_home_pie5 import draw_pie5
from draw_home_pie6 import draw_pie6
from draw_home_pie7 import draw_pie7
from draw_home_pie8 import draw_pie8
from draw_bar import draw_bar
from draw_line import draw_line
from draw_pie import draw_pie
if __name__ == '__main__':
draw_pie1('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie1.js') # home的扇形图
draw_pie2('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie2.js') # home的扇形图
draw_pie3('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie3.js') # home的扇形图
draw_pie4('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie4.js') # home的扇形图
draw_pie5('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie5.js') # home的扇形图
draw_pie6('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie6.js') # home的扇形图
draw_pie7('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie7.js') # home的扇形图
draw_pie8('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_pie8.js') # home的扇形图
draw_home_line('/home/gu/PycharmProjects/dbtest2/static/drawjs/home_line.js') # home的折线图
draw_bar('/home/gu/PycharmProjects/dbtest2/static/drawjs/bar.js') # 第二个页面的开心柱状图
draw_line('/home/gu/PycharmProjects/dbtest2/static/drawjs/line.js') # 第二个页面的开心折线图
draw_pie('/home/gu/PycharmProjects/dbtest2/static/drawjs/pie.js') # 第二个页面的开心扇形图图