def run_experiment(dataset, top_n_features, linear=False):
ds_name, words, data, train, _, statistical_type, _ = dataset
data = data[:, 0:top_n_features]
words = words[0:top_n_features]
train = train[:, 0:top_n_features]
ds_context = Context()
ds_context.statistical_type = statistical_type
add_domains(data, ds_context)
spn = learn_mspn(train, ds_context, linear=linear, memory=memory)
save_exp(spn, ds_name, top_n_features, words, data)
def run_experiment_binary(ds_file, min_instances=200, threshold=0.3):
ds_name, words, data, train, _, statistical_type, _ = get_binary_data(
ds_file)
ds_context = Context()
ds_context.statistical_type = statistical_type
add_domains(data, ds_context)
print("train data shape", train.shape)
spn = learn_mspn(train,
ds_context,
min_instances_slice=min_instances,
threshold=threshold,
linear=True,
memory=memory)
print(fpga_count_ops(spn))
save_exp(spn, ds_name, min_instances, words, data)
@author: Alejandro Molina
'''
from spn.algorithms import Inference
from spn.algorithms.StructureLearning import learn_structure
from spn.algorithms.splitting.Clustering import get_split_rows_KMeans
from spn.algorithms.splitting.RDC import get_split_cols_RDC
from spn.data.datasets import get_nips_data
from spn.structure.Base import Context
from spn.structure.leaves.Histograms import add_domains, create_histogram_leaf
if __name__ == '__main__':
import numpy as np
ds_name, words, data, train, _, statistical_type, _ = get_nips_data()
print(words)
print(data)
ds_context = Context()
ds_context.statistical_type = np.asarray(["discrete"] * data.shape[1])
add_domains(data, ds_context)
spn = learn_structure(data, ds_context, get_split_rows_KMeans(),
get_split_cols_RDC(), create_histogram_leaf)
# print(to_str_equation(spn, words))
print(Inference.likelihood(spn, data[0:100, :]))
"FROZEN_LAKE",
D,
train,
test,
np.asarray(words),
np.asarray(["discrete"] * F),
np.asarray(["categorical"] * F),
)
if __name__ == "__main__":
ds_name, data, train, test, words, statistical_type, distribution_family = get_RL_data(
)
ds_context = Context()
ds_context.statistical_type = statistical_type
ds_context.distribution_family = distribution_family
add_domains(data, ds_context)
spn = learn(train, ds_context, min_instances_slice=100, linear=True)
print(get_structure_stats(spn))
# print(to_str_ref_graph(spn, histogram_to_str))
spn_marg = marginalize(spn, set([0]))
# print(to_str_equation(spn_marg, histogram_to_str))
def eval_conditional(data):
