def test():
anp = np.random.randn(4, 5)
b = random.randint(1, 13)
a = lg.array(anp)
np.not_equal(anp, b, out=anp)
lg.not_equal(a, b, out=a)
assert np.array_equal(a, anp)
return
def test():
anp = np.random.randn(4, 5)
b = random.randint(1, 13)
a = lg.array(anp)
# test with scalar on the rhs
assert np.array_equal(lg.not_equal(a, b), np.not_equal(anp, b))
# test with scalar on the lhs
assert np.array_equal(lg.not_equal(b, a), np.not_equal(b, anp))
return
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
assert np.array_equal(lg.not_equal(a, b), np.not_equal(anp, bnp))
return
def run_kmeans(C, D, T, I, N, S, benchmarking): # noqa: E741
print("Running kmeans...")
print("Number of data points: " + str(N))
print("Number of dimensions: " + str(D))
print("Number of centroids: " + str(C))
print("Max iterations: " + str(I))
start = datetime.datetime.now()
data, centroids = initialize(N, D, C, T)
data_dots = np.square(np.linalg.norm(data, ord=2, axis=1))
data_index = np.linspace(0, N - 1, N, dtype=np.int)
labels = None
iteration = 0
prior_distance_sum = None
# We run for max iterations or until we converge
# We only test convergence every S iterations
while iteration < I:
pairwise_distances = calculate_distances(data, centroids, data_dots)
new_labels, distances = relabel(pairwise_distances, data_index)
distance_sum = np.sum(distances)
centroids = find_centroids(data, new_labels, C, D)
if iteration > 0 and iteration % S == 0:
changes = np.not_equal(labels, new_labels)
total_changes = np.sum(changes)
delta = distance_sum / prior_distance_sum
print("Iteration " + str(iteration) + " produced " +
str(total_changes) + " changes, and total distance is " +
str(distance_sum))
# We ignore the result of the threshold test in the case that we
# are running performance benchmarks to measure performance for a
# certain number of iterations
if delta > 1 - 0.000001 and not benchmarking:
print("Threshold triggered, terminating iterations early")
break
prior_distance_sum = distance_sum
labels = new_labels
iteration += 1
# This final distance sum also synchronizes the results
print("Final distance sum at iteration " + str(iteration) + ": " +
str(prior_distance_sum))
stop = datetime.datetime.now()
delta = stop - start
total = delta.total_seconds() * 1000.0
print("Elapsed Time: " + str(total) + " ms")
return total
def test():
a = random.randint(1, 13)
b = random.randint(1, 13)
assert np.array_equal(lg.not_equal(a, b), np.not_equal(a, b))
return