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from gudhi.tensorflow import *
import numpy as np
import tensorflow as tf
import gudhi as gd
def test_rips_diff():
Xinit = np.array([[1.,1.],[2.,2.]], dtype=np.float32)
X = tf.Variable(initial_value=Xinit, trainable=True)
rl = RipsLayer(maximum_edge_length=2., homology_dimensions=[0])
with tf.GradientTape() as tape:
dgm = rl.call(X)[0][0]
loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
grads = tape.gradient(loss, [X])
assert tf.norm(grads[0]-tf.constant([[-.5,-.5],[.5,.5]]),1) <= 1e-6
def test_cubical_diff():
Xinit = np.array([[0.,2.,2.],[2.,2.,2.],[2.,2.,1.]], dtype=np.float32)
X = tf.Variable(initial_value=Xinit, trainable=True)
cl = CubicalLayer(homology_dimensions=[0])
with tf.GradientTape() as tape:
dgm = cl.call(X)[0][0]
loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
grads = tape.gradient(loss, [X])
assert tf.norm(grads[0]-tf.constant([[0.,0.,0.],[0.,.5,0.],[0.,0.,-.5]]),1) <= 1e-6
def test_nonsquare_cubical_diff():
Xinit = np.array([[-1.,1.,0.],[1.,1.,1.]], dtype=np.float32)
X = tf.Variable(initial_value=Xinit, trainable=True)
cl = CubicalLayer(homology_dimensions=[0])
with tf.GradientTape() as tape:
dgm = cl.call(X)[0][0]
loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
grads = tape.gradient(loss, [X])
assert tf.norm(grads[0]-tf.constant([[0.,0.5,-0.5],[0.,0.,0.]]),1) <= 1e-6
def test_st_diff():
st = gd.SimplexTree()
st.insert([0])
st.insert([1])
st.insert([2])
st.insert([3])
st.insert([4])
st.insert([5])
st.insert([6])
st.insert([7])
st.insert([8])
st.insert([9])
st.insert([10])
st.insert([0, 1])
st.insert([1, 2])
st.insert([2, 3])
st.insert([3, 4])
st.insert([4, 5])
st.insert([5, 6])
st.insert([6, 7])
st.insert([7, 8])
st.insert([8, 9])
st.insert([9, 10])
Finit = np.array([6.,4.,3.,4.,5.,4.,3.,2.,3.,4.,5.], dtype=np.float32)
F = tf.Variable(initial_value=Finit, trainable=True)
sl = LowerStarSimplexTreeLayer(simplextree=st, homology_dimensions=[0])
with tf.GradientTape() as tape:
dgm = sl.call(F)[0][0]
loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
grads = tape.gradient(loss, [F])
assert tf.math.reduce_all(tf.math.equal(grads[0].indices, tf.constant([2,4])))
assert tf.math.reduce_all(tf.math.equal(grads[0].values, tf.constant([-1.,1.])))
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