#eps = -0.01
#reconstruction = numpy.reshape(ae.reconstruct(pt + eps), (28,-1))
#grid[num + 1].imshow(reconstruction)
#eps = 0.05
#reconstruction = numpy.reshape(ae.reconstruct(pt + eps), (28,-1))
#grid[num + 2].imshow(reconstruction)
#eps = -0.05
#reconstruction = numpy.reshape(ae.reconstruct(pt + eps), (28,-1))
#grid[num + 3].imshow(reconstruction)
#plt.show()
# Show the contraction by putting a sphere of radius eps around the target
# and measure the variation in encoding between the target and new point
print "Epsilon, Variation in Encoding"
encoded = ae.encode(pt)
vnum = 10
v = []
for j in range(0, vnum):
vdir = [random.uniform(-1,1) for i in range(0, len(pt))]
vlen = pnorm(2, vdir)
v.append([i/vlen for i in vdir])
for h in range(30, 0, -2):
h_contr = 0
for j in range(0, vnum):
encoded_v = ae.encode([i*h for i in v[j]])
h_contr += pnorm(2, encoded-encoded_v)/h
print h, h_contr/vnum
#eps = -0.1
#while eps >= 0.1:
W=W,
c=c,
b=b,
learning_rate=learning_rate,
jacobi_penalty=jacobi_penalty,
batch_size=batch_size,
epochs=epochs,
schatten_p=schatten_p )
# 2) Load the datasets (such as MNIST)
# read_amat_file is in helper_functions
[rX, rY] = read_amat_file(training_file_name, training_sample_size)
[tX, tY] = read_amat_file(testing_file_name, testing_sample_size)
# For each training point, encode
encoded_rX = [ae.encode(x) for x in rX]
#for x in rX:
# encoded_rX.append(ae.encode(x))
# 3) Measures the accuracy of the KNN on the test set
# For each testing point, encode and see how it compares
correct = 0
incorrect = 0
total = 0
j = 0
while j < len(tX):
x = tX[j]
encoded_x = ae.encode(x)
# Find the closest training point
distances = {}
i = 0
