def test_log_vector_categorical():
# Construct a minimal SPN
c1 = Categorical(p=[0.35, 0.55, 0.1], scope=0)
c2 = Categorical(p=[0.25, 0.625, 0.125], scope=1)
c3 = Categorical(p=[0.5, 0.2, 0.3], scope=2)
c4 = Categorical(p=[0.6, 0.15, 0.25], scope=3)
c5 = Categorical(p=[0.7, 0.11, 0.19], scope=4)
c6 = Categorical(p=[0.8, 0.14, 0.06], scope=5)
p = Product(children=[c1, c2, c3, c4, c5, c6])
# Randomly sample input values.
inputs = np.column_stack((
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
np.random.randint(3, size=30),
)).astype("int32")
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CPUCompiler().log_likelihood(p, inputs, supportMarginal=False)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(p, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(np.isclose(np.exp(results), np.exp(reference)))
def test_log_vector_histogram():
# Construct a minimal SPN.
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=0)
h2 = Histogram([0., 1., 2.], [0.45, 0.55], [1, 1], scope=1)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=0)
h4 = Histogram([0., 1., 2.], [0.875, 0.125], [1, 1], scope=1)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
inputs = np.column_stack((
np.random.randint(2, size=30),
np.random.randint(2, size=30),
)).astype("float64")
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CPUCompiler(maxTaskSize=5).log_likelihood(spn, inputs, supportMarginal=False)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(np.isclose(np.exp(results), np.exp(reference)))
def test_vector_slp_plants():
# Locate test resources located in same directory as this script.
scriptPath = os.path.realpath(os.path.dirname(__file__))
# Deserialize model
model = BinaryDeserializer(
os.path.join(
scriptPath,
"plants_100_200_4_3_3_3_1_True.bin")).deserialize_from_file()
spn = model.root
inputs = np.genfromtxt(os.path.join(scriptPath, "input.csv"),
delimiter=",",
dtype="float64")
reference = np.genfromtxt(os.path.join(
scriptPath, "plants_100_200_4_3_3_3_1_True_output.csv"),
delimiter=",",
dtype="float64")
reference = reference.reshape(1000)
# Compile the kernel.
options = {}
options["slp-max-look-ahead"] = 10
options["slp-max-node-size"] = 10000
options["slp-max-attempts"] = 5
options["slp-max-successful-iterations"] = 1
options["slp-reorder-dfs"] = True
options["slp-allow-duplicate-elements"] = False
options["slp-allow-topological-mixing"] = False
options["slp-use-xor-chains"] = True
# Compile the kernel with batch size 1 to enable SLP vectorization.
compiler = CPUCompiler(vectorize=True,
computeInLogSpace=True,
vectorLibrary="LIBMVEC",
**options)
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")
def test_partitioned_vector_NIPS5():
# Locate test resources located in same directory as this script.
scriptPath = os.path.realpath(os.path.dirname(__file__))
# Deserialize model
query = BinaryDeserializer(os.path.join(
scriptPath, "NIPS5.bin")).deserialize_from_file()
spn = query.graph.root
inputs = np.genfromtxt(os.path.join(scriptPath, "inputdata.txt"),
delimiter=";",
dtype="int32")
# Execute the compiled Kernel.
results = CPUCompiler(computeInLogSpace=False,
vectorize=True,
maxTaskSize=3).log_likelihood(spn,
inputs,
supportMarginal=False)
# Compute the reference results using the inference from SPFlow.
reference = np.genfromtxt(os.path.join(scriptPath, "outputdata.txt"),
delimiter=";",
dtype="float64")
reference = reference.reshape(10000)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(
np.isclose(np.exp(results), np.exp(reference)))
def test_cpu_gaussian():
# Construct a minimal SPN using two Gaussian leaves.
g1 = Gaussian(mean=0.5, stdev=1, scope=0)
g2 = Gaussian(mean=0.125, stdev=0.25, scope=1)
g3 = Gaussian(mean=0.345, stdev=0.24, scope=2)
g4 = Gaussian(mean=0.456, stdev=0.1, scope=3)
g5 = Gaussian(mean=0.94, stdev=0.48, scope=4)
g6 = Gaussian(mean=0.56, stdev=0.42, scope=5)
g7 = Gaussian(mean=0.76, stdev=0.14, scope=6)
g8 = Gaussian(mean=0.32, stdev=0.8, scope=7)
g9 = Gaussian(mean=0.58, stdev=0.9, scope=8)
g10 = Gaussian(mean=0.14, stdev=0.2, scope=9)
p = Product(children=[g1, g2, g3, g4, g5, g6, g7, g8, g9, g10])
# Randomly sample input values from the two Gaussian (normal) distributions.
inputs = np.column_stack(
(np.random.normal(0.5, 1, 30), np.random.normal(0.125, 0.25, 30),
np.random.normal(0.345, 0.24, 30), np.random.normal(0.456, 0.1, 30),
np.random.normal(0.94, 0.48, 30), np.random.normal(0.56, 0.42, 30),
np.random.normal(0.76, 0.14, 30), np.random.normal(0.32, 0.8, 30),
np.random.normal(0.58, 0.9,
30), np.random.normal(0.14, 0.2,
30))).astype("float64")
# Insert some NaN in random places into the input data.
inputs.ravel()[np.random.choice(inputs.size, 10, replace=False)] = np.nan
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CPUCompiler(computeInLogSpace=False,
vectorize=False).log_likelihood(p,
inputs,
supportMarginal=True,
batchSize=10)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(p, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(
np.isclose(np.exp(results), np.exp(reference)))
def test_vector_slp_mini():
g0 = Gaussian(mean=0.13, stdev=0.5, scope=0)
g1 = Gaussian(mean=0.14, stdev=0.25, scope=2)
g2 = Gaussian(mean=0.11, stdev=1.0, scope=3)
g3 = Gaussian(mean=0.12, stdev=0.75, scope=1)
spn = Sum(children=[g0, g1, g2, g3], weights=[0.2, 0.4, 0.1, 0.3])
# Randomly sample input values from Gaussian (normal) distributions.
num_samples = 100
inputs = np.column_stack(
(np.random.normal(loc=0.5, scale=1, size=num_samples),
np.random.normal(loc=0.125, scale=0.25, size=num_samples),
np.random.normal(loc=0.345, scale=0.24, size=num_samples),
np.random.normal(loc=0.456, scale=0.1,
size=num_samples))).astype("float64")
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(num_samples)
# Compile the kernel with batch size 1 to enable SLP vectorization.
compiler = CPUCompiler(vectorize=True,
computeInLogSpace=True,
vectorLibrary="LIBMVEC")
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
print(
f"evaluation #{i}: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}",
end='\r')
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")
def test_vector_fashion_mnist():
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Locate test resources located in same directory as this script.
scriptPath = os.path.realpath(os.path.dirname(__file__))
# Deserialize model
model = BinaryDeserializer(
os.path.join(
scriptPath,
"nltcs_100_200_2_10_8_8_1_True.bin")).deserialize_from_file()
spn = model.root
inputs = np.genfromtxt(os.path.join(scriptPath, "input.csv"),
delimiter=",",
dtype="float64")
reference = np.genfromtxt(os.path.join(
scriptPath, "nltcs_100_200_2_10_8_8_1_True_output.csv"),
delimiter=",",
dtype="float64")
reference = reference.reshape(10000)
# Compile the kernel.
compiler = CPUCompiler(vectorize=True, computeInLogSpace=True)
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")
# This file is part of the SPNC project under the Apache License v2.0 by the
# Embedded Systems and Applications Group, TU Darmstadt.
# For the full copyright and license information, please view the LICENSE
# file that was distributed with this source code.
# SPDX-License-Identifier: Apache-2.0
# ==============================================================================
import numpy as np
import os
import pytest
import time
from spnc.cpu import CPUCompiler
from xspn.serialization.binary.BinarySerialization import BinaryDeserializer
@pytest.mark.skipif(not CPUCompiler.isVectorizationSupported(),
reason="CPU vectorization not supported")
def test_vector_slp_fashion_mnist():
# Locate test resources located in same directory as this script.
scriptPath = os.path.realpath(os.path.dirname(__file__))
# Deserialize model
model = BinaryDeserializer(
os.path.join(scriptPath, "fashion_mnist_200_100_4_5_10_9_1_True.bin")
).deserialize_from_file()
spn = model.root
inputs = np.genfromtxt(os.path.join(scriptPath, "input.csv"),
delimiter=",",
dtype="float64")
reference = np.genfromtxt(os.path.join(
# file that was distributed with this source code.
# SPDX-License-Identifier: Apache-2.0
# ==============================================================================
import numpy as np
from spn.structure.Base import Product, Sum
from spn.structure.leaves.histogram.Histograms import Histogram
from spn.algorithms.Inference import log_likelihood
from spnc.cpu import CPUCompiler
import pytest
@pytest.mark.skipif(not CPUCompiler.isVectorizationSupported(), reason="CPU vectorization not supported")
def test_log_vector_histogram():
# Construct a minimal SPN.
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=0)
h2 = Histogram([0., 1., 2.], [0.45, 0.55], [1, 1], scope=1)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=0)
h4 = Histogram([0., 1., 2.], [0.875, 0.125], [1, 1], scope=1)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
inputs = np.column_stack((
np.random.randint(2, size=30),
np.random.randint(2, size=30),
)).astype("float64")
def test_vector_slp_tree():
g0 = Gaussian(mean=0.11, stdev=1, scope=0)
g1 = Gaussian(mean=0.12, stdev=0.75, scope=1)
g2 = Gaussian(mean=0.13, stdev=0.5, scope=2)
g3 = Gaussian(mean=0.14, stdev=0.25, scope=3)
g4 = Gaussian(mean=0.15, stdev=1, scope=4)
g5 = Gaussian(mean=0.16, stdev=0.25, scope=5)
g6 = Gaussian(mean=0.17, stdev=0.5, scope=6)
g7 = Gaussian(mean=0.18, stdev=0.75, scope=7)
g8 = Gaussian(mean=0.19, stdev=1, scope=8)
p0 = Product(children=[g0, g1, g2, g4])
p1 = Product(children=[g3, g4, g4, g5])
p2 = Product(children=[g6, g4, g7, g8])
p3 = Product(children=[g8, g6, g4, g2])
s0 = Sum(children=[g0, g1, g2, p0], weights=[0.25, 0.25, 0.25, 0.25])
s1 = Sum(children=[g3, g4, g5, p1], weights=[0.25, 0.25, 0.25, 0.25])
s2 = Sum(children=[g6, g7, g8, p2], weights=[0.25, 0.25, 0.25, 0.25])
s3 = Sum(children=[g0, g4, g8, p3], weights=[0.25, 0.25, 0.25, 0.25])
spn = Product(children=[s0, s1, s2, s3])
# Randomly sample input values from Gaussian (normal) distributions.
num_samples = 100
inputs = np.column_stack(
(np.random.normal(loc=0.5, scale=1, size=num_samples),
np.random.normal(loc=0.125, scale=0.25, size=num_samples),
np.random.normal(loc=0.345, scale=0.24, size=num_samples),
np.random.normal(loc=0.456, scale=0.1, size=num_samples),
np.random.normal(loc=0.94, scale=0.48, size=num_samples),
np.random.normal(loc=0.56, scale=0.42, size=num_samples),
np.random.normal(loc=0.76, scale=0.14, size=num_samples),
np.random.normal(loc=0.32, scale=0.58, size=num_samples),
np.random.normal(loc=0.58, scale=0.219, size=num_samples),
np.random.normal(loc=0.14, scale=0.52, size=num_samples),
np.random.normal(loc=0.24, scale=0.42, size=num_samples),
np.random.normal(loc=0.34, scale=0.1, size=num_samples),
np.random.normal(loc=0.44, scale=0.9, size=num_samples),
np.random.normal(loc=0.54, scale=0.7, size=num_samples),
np.random.normal(loc=0.64, scale=0.5, size=num_samples),
np.random.normal(loc=0.74, scale=0.4,
size=num_samples))).astype("float64")
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(num_samples)
# Compile the kernel with batch size 1 to enable SLP vectorization.
compiler = CPUCompiler(vectorize=True,
computeInLogSpace=True,
vectorLibrary="LIBMVEC")
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
print(
f"evaluation #{i}: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}",
end='\r')
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")
def test_vector_slp_escaping_users():
g0 = Gaussian(mean=0.00, stdev=1, scope=0)
g1 = Gaussian(mean=0.01, stdev=0.75, scope=1)
g2 = Gaussian(mean=0.02, stdev=0.5, scope=2)
g3 = Gaussian(mean=0.03, stdev=0.25, scope=3)
g4 = Gaussian(mean=0.04, stdev=1, scope=4)
g5 = Gaussian(mean=0.05, stdev=0.25, scope=5)
g6 = Gaussian(mean=0.06, stdev=0.5, scope=6)
g7 = Gaussian(mean=0.07, stdev=0.75, scope=7)
g8 = Gaussian(mean=0.08, stdev=1, scope=8)
g9 = Gaussian(mean=0.09, stdev=0.75, scope=9)
g10 = Gaussian(mean=0.10, stdev=1, scope=10)
g11 = Gaussian(mean=0.11, stdev=1, scope=11)
h0 = Histogram([0., 1., 2.], [0.1, 0.9], [1, 1], scope=12)
h1 = Histogram([0., 1., 2.], [0.2, 0.8], [1, 1], scope=13)
h2 = Histogram([0., 1., 2.], [0.3, 0.7], [1, 1], scope=14)
h3 = Histogram([0., 1., 2.], [0.4, 0.6], [1, 1], scope=15)
h4 = Histogram([0., 1., 2.], [0.5, 0.5], [1, 1], scope=16)
h5 = Histogram([0., 1., 2.], [0.6, 0.4], [1, 1], scope=17)
h6 = Histogram([0., 1., 2.], [0.7, 0.3], [1, 1], scope=18)
h7 = Histogram([0., 1., 2.], [0.8, 0.2], [1, 1], scope=19)
c0 = Categorical(p=[0.1, 0.1, 0.8], scope=20)
c1 = Categorical(p=[0.2, 0.2, 0.6], scope=21)
c2 = Categorical(p=[0.3, 0.3, 0.4], scope=22)
c3 = Categorical(p=[0.4, 0.4, 0.2], scope=23)
c4 = Categorical(p=[0.5, 0.4, 0.1], scope=24)
c5 = Categorical(p=[0.6, 0.3, 0.1], scope=25)
c6 = Categorical(p=[0.7, 0.2, 0.1], scope=26)
c7 = Categorical(p=[0.8, 0.1, 0.1], scope=27)
s0 = Sum(children=[g8, h4], weights=[0.5, 0.5])
s1 = Sum(children=[g9, h5], weights=[0.5, 0.5])
s2 = Sum(children=[g10, c6], weights=[0.5, 0.5])
s3 = Sum(children=[g11, h7], weights=[0.5, 0.5])
s4 = Sum(children=[s0, c4], weights=[0.5, 0.5])
s5 = Sum(children=[s1, c5], weights=[0.5, 0.5])
s6 = Sum(children=[s2, g6], weights=[0.5, 0.5])
s7 = Sum(children=[s3, c7], weights=[0.5, 0.5])
s8 = Sum(children=[s4, g4], weights=[0.5, 0.5])
s9 = Sum(children=[s5, g5], weights=[0.5, 0.5])
s10 = Sum(children=[s6, h6], weights=[0.5, 0.5])
s11 = Sum(children=[s7, g7], weights=[0.5, 0.5])
p0 = Product(children=[h0, s8])
p1 = Product(children=[c1, s9])
p2 = Product(children=[c2, s10])
p3 = Product(children=[g3, s11])
p4 = Product(children=[p0, g0])
p5 = Product(children=[p1, g1])
p6 = Product(children=[p2, h2])
p7 = Product(children=[p3, c3])
p8 = Product(children=[p4, c0])
p9 = Product(children=[p5, h1])
p10 = Product(children=[p6, g2])
p11 = Product(children=[p7, h3])
s12 = Sum(children=[p8, p9], weights=[0.5, 0.5])
s13 = Sum(children=[p10, p11], weights=[0.5, 0.5])
s14 = Sum(children=[s12, p2], weights=[0.5, 0.5])
s15 = Sum(children=[s13, s2], weights=[0.5, 0.5])
spn = Product(children=[s14, s15])
# Randomly sample input values from Gaussian (normal) distributions.
num_samples = 100
inputs = np.column_stack((
# gaussian
np.random.normal(loc=0.5, scale=1, size=num_samples),
np.random.normal(loc=0.125, scale=0.25, size=num_samples),
np.random.normal(loc=0.345, scale=0.24, size=num_samples),
np.random.normal(loc=0.456, scale=0.1, size=num_samples),
np.random.normal(loc=0.94, scale=0.48, size=num_samples),
np.random.normal(loc=0.56, scale=0.42, size=num_samples),
np.random.normal(loc=0.76, scale=0.14, size=num_samples),
np.random.normal(loc=0.32, scale=0.58, size=num_samples),
np.random.normal(loc=0.58, scale=0.219, size=num_samples),
np.random.normal(loc=0.14, scale=0.52, size=num_samples),
np.random.normal(loc=0.24, scale=0.42, size=num_samples),
np.random.normal(loc=0.34, scale=0.1, size=num_samples),
# histogram
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
# categorical
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples))).astype("float64")
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(num_samples)
# Compile the kernel with batch size 1 to enable SLP vectorization.
compiler = CPUCompiler(vectorize=True,
computeInLogSpace=True,
vectorLibrary="LIBMVEC")
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
print(
f"evaluation #{i}: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}",
end='\r')
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")
def test_vector_slp_speaker():
# Locate test resources located in same directory as this script.
scriptPath = os.path.realpath(os.path.dirname(__file__))
# Read the trained SPN from file
model = BinaryDeserializer(os.path.join(
scriptPath, "speaker_FADG0.bin")).deserialize_from_file()
spn = model.graph.root
# Randomly sample input values from Gaussian (normal) distributions.
num_samples = 10000
inputs = np.column_stack((
# 26 gaussian inputs
np.random.normal(loc=0.01, scale=1.00, size=num_samples),
np.random.normal(loc=0.02, scale=0.90, size=num_samples),
np.random.normal(loc=0.03, scale=0.80, size=num_samples),
np.random.normal(loc=0.04, scale=0.70, size=num_samples),
np.random.normal(loc=0.05, scale=0.60, size=num_samples),
np.random.normal(loc=0.06, scale=0.50, size=num_samples),
np.random.normal(loc=0.07, scale=0.40, size=num_samples),
np.random.normal(loc=0.08, scale=0.30, size=num_samples),
np.random.normal(loc=0.09, scale=0.20, size=num_samples),
np.random.normal(loc=0.10, scale=0.10, size=num_samples),
np.random.normal(loc=0.11, scale=1.00, size=num_samples),
np.random.normal(loc=0.12, scale=0.90, size=num_samples),
np.random.normal(loc=0.13, scale=0.80, size=num_samples),
np.random.normal(loc=0.14, scale=0.70, size=num_samples),
np.random.normal(loc=0.15, scale=0.60, size=num_samples),
np.random.normal(loc=0.16, scale=0.50, size=num_samples),
np.random.normal(loc=0.17, scale=0.40, size=num_samples),
np.random.normal(loc=0.18, scale=0.30, size=num_samples),
np.random.normal(loc=0.19, scale=0.20, size=num_samples),
np.random.normal(loc=0.20, scale=0.10, size=num_samples),
np.random.normal(loc=0.21, scale=1.00, size=num_samples),
np.random.normal(loc=0.22, scale=0.90, size=num_samples),
np.random.normal(loc=0.23, scale=0.80, size=num_samples),
np.random.normal(loc=0.24, scale=0.70, size=num_samples),
np.random.normal(loc=0.25, scale=0.60, size=num_samples),
np.random.normal(loc=0.26, scale=0.50, size=num_samples),
)).astype("float64")
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(num_samples)
# Compile the kernel with batch size 1 to enable SLP vectorization.
compiler = CPUCompiler(vectorize=True, computeInLogSpace=True)
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")
