from __future__ import print_function
import time
import random
from pyRMSD.condensedMatrix import CondensedMatrix
import pyRMSD.benchmark.alias.condensedMatrix as PythonCondensedMatrix
import pyRMSD.benchmark.alias.CythonCondensedMatrix as CythonCondensedMatrixes #@UnresolvedImport
if __name__ == '__main__':
print("Creating data...")
row_size = 10000
matrix_elem_size = row_size * (row_size - 1) / 2
contents = random.sample(xrange(matrix_elem_size + 1), matrix_elem_size)
matrixes = {}
matrixes[
"Python ConndensedMatrix"] = PythonCondensedMatrix.CondensedMatrix(
contents)
matrixes["C CondensedMatrix"] = CondensedMatrix(contents)
matrixes[
"CythonCondensedMatrix"] = CythonCondensedMatrixes.CythonCondensedMatrix(
contents)
matrixes[
"CythonCondensedMatrixWithDiagonal"] = CythonCondensedMatrixes.CythonCondensedMatrixWithDiagonal(
contents)
print("========================")
print("Serial access Benchmark")
print("========================")
for matrix_type in matrixes:
irange = range(row_size)
jrange = range(row_size)
from pyRMSD.condensedMatrix import CondensedMatrix
import pyRMSD.benchmark.alias.condensedMatrix as PythonCondensedMatrix
from pyRMSD.benchmark.alias.neighbourOps import choose_node_with_higher_cardinality,\
get_neighbors_for_node
if __name__ == '__main__':
print("Creating data...")
row_size = 20000
matrix_elem_size = row_size * (row_size - 1) / 2
contents = numpy.array(
random.sample(xrange(matrix_elem_size + 1), matrix_elem_size))
float_contents = contents / float(max(contents))
del contents
matrix = CondensedMatrix(float_contents)
matrix2 = PythonCondensedMatrix.CondensedMatrix(float_contents)
remaining_nodes = range(row_size)
print("======================================")
print("'get_neighbors_for_node' benchmark")
print("======================================")
time_start = time.time()
neighbors1 = matrix.get_neighbors_for_node(1, remaining_nodes, 0.5)
time_end = time.time()
print("Fast Neighbor search for Fast matrix took %.3fs" %
(time_end - time_start))
time_start = time.time()
neighbors2 = matrix2.get_neighbors_for_node(1, remaining_nodes, 0.5)
time_end = time.time()
print("Slow Neighbor search for Slow matrix took %.3fs" %