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bg_utils.py
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bg_utils.py
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'''
Author: Panpan Zheng
Date created: 2/15/2018
Python Version: 2.7
'''
import numpy as np
import tensorflow as tf
from sklearn.neighbors.kde import KernelDensity
import matplotlib.pyplot as plt
def one_hot(x, depth):
x_one_hot = np.zeros((len(x), depth), dtype=np.int32)
x = x.astype(int)
for i in range(x_one_hot.shape[0]):
x_one_hot[i, x[i]] = 1
return x_one_hot
def xavier_init(size): # initialize the weight-matrix W.
in_dim = size[0]
xavier_stddev = 1. / tf.sqrt(in_dim / 2.)
return tf.random_normal(shape=size, stddev=xavier_stddev)
def sample_Z(m, n): # generating the input for G.
return np.random.uniform(-1., 1., size=[m, n])
def sample_shuffle_spv(X, labels):
n_samples = len(X)
s = np.arange(n_samples)
np.random.shuffle(s)
return np.array(X[s]), labels[s]
def sample_shuffle_uspv(X):
n_samples = len(X)
s = np.arange(n_samples)
np.random.shuffle(s)
return np.array(X[s])
def kde_density_estimator(X,kernel='gaussian',bandwidth=0.2):
return KernelDensity(kernel=kernel, bandwidth=bandwidth).fit(X)
def complement_density(kde, X, sf=0.5):
# probs = map(lambda x: np.exp(kde.score([x])), X)
probs = np.exp(kde.score_samples(X))
thrld = np.median(probs)
return np.array(
map(lambda x: low_density(x, thrld, sf), probs)
)
def low_density(prob, thrld, sf):
if prob > thrld:
return sf * np.reciprocal(prob)
# return sf * (1-prob)
else:
return thrld
def pull_away_loss(g):
Nor = tf.norm(g, axis=1)
Nor_mat = tf.tile(tf.expand_dims(Nor, axis=1),
[1, tf.shape(g)[1]])
X = tf.divide(g, Nor_mat)
X_X = tf.square(tf.matmul(X, tf.transpose(X)))
mask = tf.subtract(tf.ones_like(X_X),
tf.diag(
tf.ones([tf.shape(X_X)[0]]))
)
pt_loss = tf.divide(tf.reduce_sum(tf.multiply(X_X, mask)),
tf.multiply(
tf.cast(tf.shape(X_X)[0], tf.float32),
tf.cast(tf.shape(X_X)[0]-1, tf.float32)))
return pt_loss
def draw_trend(D_real_prob, D_fake_prob, D_val_prob, fm_loss, f1):
fig = plt.figure()
fig.patch.set_facecolor('w')
# plt.subplot(311)
p1, = plt.plot(D_real_prob, "-g")
p2, = plt.plot(D_fake_prob, "--r")
p3, = plt.plot(D_val_prob, ":c")
plt.xlabel("# of epoch")
plt.ylabel("probability")
leg = plt.legend([p1, p2, p3], [r'$p(y|V_B)$', r'$p(y|\~{V})$', r'$p(y|V_M)$'], loc=1, bbox_to_anchor=(1, 1), borderaxespad=0.)
leg.draw_frame(False)
# plt.legend(frameon=False)
fig = plt.figure()
fig.patch.set_facecolor('w')
# plt.subplot(312)
p4, = plt.plot(fm_loss, "-b")
plt.xlabel("# of epoch")
plt.ylabel("feature matching loss")
# plt.legend([p4], ["d_real_prob", "d_fake_prob", "d_val_prob"], loc=1, bbox_to_anchor=(1, 1), borderaxespad=0.)
fig = plt.figure()
fig.patch.set_facecolor('w')
# plt.subplot(313)
p5, = plt.plot(f1, "-y")
plt.xlabel("# of epoch")
plt.ylabel("F1")
# plt.legend([p1, p2, p3, p4, p5], ["d_real_prob", "d_fake_prob", "d_val_prob", "fm_loss","f1"], loc=1, bbox_to_anchor=(1, 3.5), borderaxespad=0.)
plt.show()
def plot_decision_boundary(pred_func, X, y):
# Set min and max values and give it some padding
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole gid
Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)