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import numpy as np
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
from gudhi.tensorflow.perslay import *
import gudhi.representations as gdr
def test_gaussian_perslay():
diagrams = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])]
diagrams = gdr.DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diagrams)
diagrams = tf.RaggedTensor.from_tensor(tf.constant(diagrams, dtype=tf.float32))
rho = tf.identity
phi = GaussianPerslayPhi((5, 5), ((-.5, 1.5), (-.5, 1.5)), .1)
weight = PowerPerslayWeight(1.,0.)
perm_op = tf.math.reduce_sum
perslay = Perslay(phi=phi, weight=weight, perm_op=perm_op, rho=rho)
vectors = perslay(diagrams)
print(vectors.shape)
assert np.linalg.norm(vectors.numpy() - np.array(
[[[[1.7266072e-16],
[4.1706043e-09],
[1.1336876e-08],
[8.5738821e-12],
[2.1243891e-14]],
[[4.1715076e-09],
[1.0074080e-01],
[2.7384272e-01],
[3.0724244e-02],
[7.6157507e-05]],
[[8.0382870e-06],
[1.5802664e+00],
[8.2997030e-01],
[1.2395413e+01],
[3.0724116e-02]],
[[8.0269419e-06],
[1.3065740e+00],
[9.0923014e+00],
[6.1664842e-02],
[1.3949171e-06]],
[[9.0331329e-13],
[1.4954816e-07],
[1.5145997e-04],
[1.0205092e-06],
[7.8093526e-16]]]]) <= 1e-7)
test_gaussian_perslay()
def test_tent_perslay():
diagrams = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])]
diagrams = gdr.DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diagrams)
diagrams = tf.RaggedTensor.from_tensor(tf.constant(diagrams, dtype=tf.float32))
rho = tf.identity
phi = TentPerslayPhi(np.array(np.arange(-1.,2.,.1), dtype=np.float32))
weight = PowerPerslayWeight(1.,0.)
perm_op = 'top3'
perslay = Perslay(phi=phi, weight=weight, perm_op=perm_op, rho=rho)
vectors = perslay(diagrams)
assert np.linalg.norm(vectors-np.array([[0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0.09999999, 0., 0.,
0.2, 0.05, 0., 0.19999999, 0., 0.,
0.09999999, 0.02500001, 0., 0.125, 0., 0.,
0.22500002, 0., 0., 0.3, 0., 0.,
0.19999999, 0.05000001, 0., 0.10000002, 0.10000002, 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0. ]])) <= 1e-7
def test_flat_perslay():
diagrams = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])]
diagrams = gdr.DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diagrams)
diagrams = tf.RaggedTensor.from_tensor(tf.constant(diagrams, dtype=tf.float32))
rho = tf.identity
phi = FlatPerslayPhi(np.array(np.arange(-1.,2.,.1), dtype=np.float32), 100.)
weight = PowerPerslayWeight(1.,0.)
perm_op = tf.math.reduce_sum
perslay = Perslay(phi=phi, weight=weight, perm_op=perm_op, rho=rho)
vectors = perslay(diagrams)
assert np.linalg.norm(vectors-np.array([[0.0000000e+00, 0.0000000e+00, 1.8048651e-35, 3.9754645e-31, 8.7565101e-27,
1.9287571e-22, 4.2483860e-18, 9.3576392e-14, 2.0611652e-09, 4.5398087e-05,
5.0000376e-01, 1.0758128e+00, 1.9933071e+00, 1.0072457e+00, 1.9240967e+00,
1.4999963e+00, 1.0000458e+00, 1.0066929e+00, 1.9933071e+00, 1.9999092e+00,
1.0000000e+00, 9.0795562e-05, 4.1222914e-09, 1.8715316e-13, 8.4967405e-18,
3.8574998e-22, 1.7512956e-26, 7.9508388e-31, 3.6097302e-35, 0.0000000e+00]]) <= 1e-7)
def test_gmix_weight():
diagrams = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])]
diagrams = gdr.DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diagrams)
diagrams = tf.RaggedTensor.from_tensor(tf.constant(diagrams, dtype=tf.float32))
rho = tf.identity
phi = FlatPerslayPhi(np.array(np.arange(-1.,2.,.1), dtype=np.float32), 100.)
weight = GaussianMixturePerslayWeight(np.array([[.5],[.5],[5],[5]], dtype=np.float32))
perm_op = tf.math.reduce_sum
perslay = Perslay(phi=phi, weight=weight, perm_op=perm_op, rho=rho)
vectors = perslay(diagrams)
assert np.linalg.norm(vectors-np.array([[0.0000000e+00, 0.0000000e+00, 1.7869064e-35, 3.9359080e-31, 8.6693818e-27,
1.9095656e-22, 4.2061142e-18, 9.2645292e-14, 2.0406561e-09, 4.4946366e-05,
4.9502861e-01, 1.0652492e+00, 1.9753191e+00, 9.9723548e-01, 1.9043801e+00,
1.4844525e+00, 9.8947650e-01, 9.9604094e-01, 1.9703994e+00, 1.9769192e+00,
9.8850453e-01, 8.9751818e-05, 4.0749040e-09, 1.8500175e-13, 8.3990662e-18,
3.8131562e-22, 1.7311636e-26, 7.8594399e-31, 3.5682349e-35, 0.0000000e+00]]) <= 1e-7)
def test_grid_weight():
diagrams = [np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])]
diagrams = gdr.DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diagrams)
diagrams = tf.RaggedTensor.from_tensor(tf.constant(diagrams, dtype=tf.float32))
rho = tf.identity
phi = FlatPerslayPhi(np.array(np.arange(-1.,2.,.1), dtype=np.float32), 100.)
weight = GridPerslayWeight(np.array(np.random.uniform(size=[100,100]),dtype=np.float32),((-0.01, 1.01),(-0.01, 1.01)))
perm_op = tf.math.reduce_sum
perslay = Perslay(phi=phi, weight=weight, perm_op=perm_op, rho=rho)
vectors = perslay(diagrams)
assert np.linalg.norm(vectors-np.array([[0.0000000e+00, 0.0000000e+00, 1.5124093e-37, 3.3314498e-33, 7.3379791e-29,
1.6163036e-24, 3.5601592e-20, 7.8417273e-16, 1.7272621e-11, 3.8043717e-07,
4.1902456e-03, 1.7198652e-02, 1.2386327e-01, 9.2694648e-03, 1.9515079e-01,
2.0629172e-01, 2.0210314e-01, 2.0442720e-01, 5.4709727e-01, 5.4939687e-01,
2.7471092e-01, 2.4942532e-05, 1.1324385e-09, 5.1413016e-14, 2.3341474e-18,
1.0596973e-22, 4.8110000e-27, 2.1841823e-31, 9.9163230e-36, 0.0000000e+00]]) <= 1e-7)
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