def F(inputs):
self.count += 1
R = fast_train_function(inputs)
if self.count % self.k == 0:
K.batch_get_value(slow_updates)
K.batch_get_value(copy_updates)
return R
def testLinearSolverConstruction(self, x1, y1, x2, y2, a, b):
"""
Verifies that a TFLinearEquationSolver is constructed correctly.
"""
solver = clustering_centroids.TFLinearEquationSolver(
float(x1), float(y1), float(x2), float(y2))
solver_a = solver.a
self.assertAlmostEqual(K.batch_get_value([solver_a])[0], a)
self.assertAlmostEqual(K.batch_get_value([solver.b])[0], b)
def _pull_values(self, ca, pulling_indices, expected_output):
pulling_indices_np = np.array(pulling_indices)
res_tf = ca.get_clustered_weight(pulling_indices_np)
res_np = K.batch_get_value([res_tf])[0]
res_np_list = res_np.tolist()
self.assertSequenceEqual(res_np_list, expected_output)
def testLinearSolverSolveForX(self, x1, y1, x2, y2, x, y):
"""
Verifies that TFLinearEquationSolver solves the given equations correctly
for X.
"""
solver = clustering_centroids.TFLinearEquationSolver(
float(x1), float(y1), float(x2), float(y2))
for_x = solver.solve_for_x(y)
self.assertAlmostEqual(K.batch_get_value([for_x])[0], x)
def testCDFValues(self, weights, point, probability):
"""
Verifies that TFCumulativeDistributionFunction yields the expected output
for the inputs provided.
"""
cdf_calc = clustering_centroids.TFCumulativeDistributionFunction(
weights)
self.assertAlmostEqual(
probability,
K.batch_get_value([cdf_calc.get_cdf_value(point)])[0])
def testLinearSolverSolveForY(self, x1, y1, x2, y2, x, y):
solver = clustering_centroids.TFLinearEquationSolver(
float(x1), float(y1), float(x2), float(y2))
for_y = solver.solve_for_y(x)
self.assertAlmostEqual(K.batch_get_value([for_y])[0], y)
def testClusterCentroids(self, weights, number_of_clusters, centroids):
dbci = clustering_centroids.DensityBasedCentroidsInitialisation(
weights, number_of_clusters)
calc_centroids = K.batch_get_value([dbci.get_cluster_centroids()])[0]
self.assertSequenceAlmostEqual(centroids, calc_centroids, places=4)
def testCDFValues(self, weights, point, probability):
cdf_calc = clustering_centroids.TFCumulativeDistributionFunction(
weights)
self.assertAlmostEqual(
probability,
K.batch_get_value([cdf_calc.get_cdf_value(point)])[0])