def check_obj_and_reconstruction(self, obj):
str_val = spn_to_str_equation(obj)
obj_val = str_to_spn(str_val)
self.assertEqual(str_val, spn_to_str_equation(obj_val))
from spn.io.Text import str_to_spn, to_JSON, spn_to_str_equation
from spn.structure.StatisticalTypes import MetaType
from spn.structure.leaves.parametric.Inference import add_parametric_inference_support
from spn.structure.leaves.parametric.Parametric import *
from spn.structure.leaves.parametric.Text import add_parametric_text_support
if __name__ == '__main__':
add_parametric_text_support()
add_parametric_inference_support()
cat = Categorical(p=[0.1, 0.2, 0.7])
cat.scope.append(0)
print(spn_to_str_equation(cat))
catspn = str_to_spn(spn_to_str_equation(cat))
print(spn_to_str_equation(catspn))
original = Gaussian(mean=0, stdev=10)
original.scope.append(0)
s = spn_to_str_equation(original)
print(s)
recovered = str_to_spn(s)
print(str_to_spn("Gaussian(V0|mean=1;stdev=10)"))
gamma = Gamma(alpha=1, beta=2)
gamma.scope.append(0)
print(spn_to_str_equation(gamma))
lnorm = LogNormal(mean=1, stdev=2)
row_set = set.union(*instances_sets)
assert set(node.row_ids) == row_set
return row_set
whole_row_ids = _validate_row_partitioning(node)
instance_set = set(instance_set)
assert instance_set == whole_row_ids, ' Expected row ids {}, got {}'.format(
instance_set, whole_row_ids)
if __name__ == '__main__':
n = str_to_spn(
"""
(
Histogram(W1|[ 0., 1., 2.];[0.3, 0.7])
*
Histogram(W2|[ 0., 1., 2.];[0.3, 0.7])
)
""", ["W1", "W2"])
n = str_to_spn(
"""
(0.3 * Histogram(W1|[ 0., 1., 2.];[0.2, 0.8])
+
0.7 * Histogram(W1|[ 0., 1., 2.];[0.1, 0.9])
)
""", ["W1", "W2"])
print(to_JSON(n))
map_id_node = init_spn_sampling(n)
'''
Created on March 29, 2018
@author: Alejandro Molina
'''
from spn.leaves.Histograms import Histogram_str_to_spn
from spn.experiments.FPGA.GenerateSPNs import fpga_count_ops
from spn.io.Graphics import plot_spn
from spn.io.Text import str_to_spn
if __name__ == '__main__':
with open('../experiments/FPGA/spns/NIPS_30/eqq.txt', 'r') as myfile:
eq = myfile.read()
with open('../experiments/FPGA/spns/NIPS_30/all_data.txt', 'r') as myfile:
words = myfile.readline().strip()
words = words[2:]
words = words.split(';')
print(words)
spn = str_to_spn(eq, words, Histogram_str_to_spn)
print(spn)
print(fpga_count_ops(spn))
plot_spn(spn)
def load(eq, words):
return str_to_spn(eq, words)
def check_obj_and_reconstruction(self, obj):
self.assertEqual(
spn_to_str_equation(obj),
spn_to_str_equation(str_to_spn(spn_to_str_equation(obj))))
from spn.algorithms.Statistics import get_structure_stats
from spn.data.datasets import get_nips_data
from spn.io.Text import str_to_spn
if __name__ == '__main__':
with open('40_eqq.txt', 'r') as myfile:
eq = myfile.read()
with open('40_testdata.txt', 'r') as myfile:
words = myfile.readline().strip()
words = words[2:]
words = words.split(';')
# print(eq)
print(words)
spn = str_to_spn(eq, words)
print(get_structure_stats(spn))
# print(Text.toJSON(spn))
data = np.loadtxt("40_testdata.txt", delimiter=';')
ll = Inference.likelihood(spn, data)
print(ll)
print("average LL", np.mean(ll))
ds_name, words, data, _, _, _, _ = get_nips_data()
top_n_features = 40
import tensorflow as tf
from spn.io.Text import spn_to_str_equation, str_to_spn
from spn.gpu.TensorFlow import optimize_tf
import sys
import numpy as np
#Increase recursion limit as otherwise conversion will fail
sys.setrecursionlimit(15000)
#Load string, convert to SPN
with open('./ca.txt', 'r') as myfile:
data = myfile.read().replace('\n', '')
thespn = str_to_spn(data)
#Load data
testdata = np.load('./test.npy')
testdata += 0.0
print(testdata.dtype)
print(np.amax(testdata))
#testdata = whiten(testdata)
print(np.amax(testdata))
testdata = testdata.astype(np.float32)
print(testdata.dtype)