def testRejection2D(self):
num_samples = int(1e5) # Chosen for a small min detectable discrepancy
det_bounds = np.array(
[0.01, 0.02, 0.03, 0.04, 0.05, 0.3, 0.35, 0.4, 0.5],
dtype=np.float32)
exact_volumes = two_by_two_volume(det_bounds)
(rej_weights, rej_proposal_volume
) = corr.correlation_matrix_volume_rejection_samples(det_bounds,
2,
[num_samples, 9],
dtype=np.float32,
seed=43)
# shape of rej_weights: [num_samples, 9, 2, 2]
chk1 = st.assert_true_mean_equal_by_dkwm(rej_weights,
low=0.,
high=rej_proposal_volume,
expected=exact_volumes,
false_fail_rate=1e-6)
chk2 = tf1.assert_less(
st.min_discrepancy_of_true_means_detectable_by_dkwm(
num_samples,
low=0.,
high=rej_proposal_volume,
# Correct the false fail rate due to different broadcasting
false_fail_rate=1.1e-7,
false_pass_rate=1e-6),
0.036)
with tf.control_dependencies([chk1, chk2]):
rej_weights = tf.identity(rej_weights)
self.evaluate(rej_weights)
def testRejection4D(self):
num_samples = int(1e5) # Chosen for a small min detectable discrepancy
det_bounds = np.array([0.0], dtype=np.float32)
exact_volumes = [four_by_four_volume()]
(rej_weights, rej_proposal_volume
) = corr.correlation_matrix_volume_rejection_samples(det_bounds,
4,
[num_samples, 1],
dtype=np.float32,
seed=45)
# shape of rej_weights: [num_samples, 1, 4, 4]
chk1 = st.assert_true_mean_equal_by_dkwm(rej_weights,
low=0.,
high=rej_proposal_volume,
expected=exact_volumes,
false_fail_rate=1e-6)
chk2 = tf1.assert_less(
st.min_discrepancy_of_true_means_detectable_by_dkwm(
num_samples,
low=0.,
high=rej_proposal_volume,
false_fail_rate=1e-6,
false_pass_rate=1e-6),
# Going for about a 10% relative error
1.1)
with tf.control_dependencies([chk1, chk2]):
rej_weights = tf.identity(rej_weights)
self.evaluate(rej_weights)
def testRejection4D(self):
num_samples = int(1e5) # Chosen for a small min detectable discrepancy
det_bounds = np.array([0.0], dtype=np.float32)
exact_volumes = [four_by_four_volume()]
(rej_weights,
rej_proposal_volume) = corr.correlation_matrix_volume_rejection_samples(
det_bounds, 4, [num_samples, 1], dtype=np.float32, seed=45)
# shape of rej_weights: [num_samples, 1, 4, 4]
chk1 = st.assert_true_mean_equal_by_dkwm(
rej_weights, low=0., high=rej_proposal_volume, expected=exact_volumes,
false_fail_rate=1e-6)
chk2 = tf.assert_less(
st.min_discrepancy_of_true_means_detectable_by_dkwm(
num_samples, low=0., high=rej_proposal_volume,
false_fail_rate=1e-6, false_pass_rate=1e-6),
# Going for about a 10% relative error
1.1)
with tf.control_dependencies([chk1, chk2]):
rej_weights = tf.identity(rej_weights)
self.evaluate(rej_weights)
def testRejection2D(self):
num_samples = int(1e5) # Chosen for a small min detectable discrepancy
det_bounds = np.array(
[0.01, 0.02, 0.03, 0.04, 0.05, 0.3, 0.35, 0.4, 0.5], dtype=np.float32)
exact_volumes = two_by_two_volume(det_bounds)
(rej_weights,
rej_proposal_volume) = corr.correlation_matrix_volume_rejection_samples(
det_bounds, 2, [num_samples, 9], dtype=np.float32, seed=43)
# shape of rej_weights: [num_samples, 9, 2, 2]
chk1 = st.assert_true_mean_equal_by_dkwm(
rej_weights, low=0., high=rej_proposal_volume, expected=exact_volumes,
false_fail_rate=1e-6)
chk2 = tf.assert_less(
st.min_discrepancy_of_true_means_detectable_by_dkwm(
num_samples, low=0., high=rej_proposal_volume,
# Correct the false fail rate due to different broadcasting
false_fail_rate=1.1e-7, false_pass_rate=1e-6),
0.036)
with tf.control_dependencies([chk1, chk2]):
rej_weights = tf.identity(rej_weights)
self.evaluate(rej_weights)
