def test_get_z_sparse():
"""
function CPTMNeuralNetwork.get_z_sparse
implements an optimized algorithm for calculating
the input z1 to the hidden layer. See section 5.1.1 in the paper
z1 = numpy.dot(W1.transpose(), x)
Test if optization works
"""
d1 = 99
d2 = 10
W1 = np.random.randn(d1, d2)
W2 = np.identity(d2)
nn = CPTMNeuralNetwork(sizes=[d1, d2, d2], weights=[W1, W2])
x = np.random.randint(5, size=(d1, 1)) # x is d1x1
word_id_count_dict = {}
for word_id, count in enumerate(x):
word_id_count_dict[word_id] = word_id_count_dict.get(word_id, 0) + count
r1 = np.dot(W1.transpose(), x)
r2 = nn.get_z_sparse(W1, word_id_count_dict)
test_passed = np.array_equal(r1, r2)
print "Test result:", test_passed, "for test_get_z_sparse"
return test_passed
def test_get_z_sparse():
"""
function CPTMNeuralNetwork.get_z_sparse
implements an optimized algorithm for calculating
the input z1 to the hidden layer. See section 5.1.1 in the paper
z1 = numpy.dot(W1.transpose(), x)
Test if optization works
"""
d1 = 99
d2 = 10
W1 = np.random.randn(d1, d2)
W2 = np.identity(d2)
nn = CPTMNeuralNetwork(sizes=[d1, d2, d2], weights=[W1, W2])
x = np.random.randint(5, size=(d1, 1)) # x is d1x1
word_id_count_dict = {}
for word_id, count in enumerate(x):
word_id_count_dict[word_id] = word_id_count_dict.get(word_id,
0) + count
r1 = np.dot(W1.transpose(), x)
r2 = nn.get_z_sparse(W1, word_id_count_dict)
test_passed = np.array_equal(r1, r2)
print "Test result:", test_passed, "for test_get_z_sparse"
return test_passed
def test_sparse_array_to_d_W1():
"""
function CPTMNeuralNetwork.sparse_array_to_d_W1
implements an optimized algorithm for Equation 10 in paper.
Test if optization works
"""
d1 = 99
d2 = 10
nn = CPTMNeuralNetwork(sizes=[d1, d2, d2])
x = np.random.randint(5, size=(d1, 1)) # x is d1x1
not_sparse = np.random.randn(1, d2) # not_sparse is 1xd2 array
word_id_count_dict = {}
for word_id, count in enumerate(x):
word_id_count_dict[word_id] = word_id_count_dict.get(word_id, 0) + count
r1 = np.dot(x, not_sparse)
r2 = nn.sparse_array_to_d_W1(word_id_count_dict, not_sparse)
test_passed = np.array_equal(r1, r2)
print "Test result:", test_passed, "for test_sparse_array_to_d_W1"
return test_passed
def test_sparse_array_to_d_W1():
"""
function CPTMNeuralNetwork.sparse_array_to_d_W1
implements an optimized algorithm for Equation 10 in paper.
Test if optization works
"""
d1 = 99
d2 = 10
nn = CPTMNeuralNetwork(sizes=[d1, d2, d2])
x = np.random.randint(5, size=(d1, 1)) # x is d1x1
not_sparse = np.random.randn(1, d2) # not_sparse is 1xd2 array
word_id_count_dict = {}
for word_id, count in enumerate(x):
word_id_count_dict[word_id] = word_id_count_dict.get(word_id,
0) + count
r1 = np.dot(x, not_sparse)
r2 = nn.sparse_array_to_d_W1(word_id_count_dict, not_sparse)
test_passed = np.array_equal(r1, r2)
print "Test result:", test_passed, "for test_sparse_array_to_d_W1"
return test_passed
