68 lines
1.5 KiB
Python
68 lines
1.5 KiB
Python
from .common import Benchmark, TYPES1, get_squares
|
|
|
|
import numpy as np
|
|
|
|
|
|
class AddReduce(Benchmark):
|
|
def setup(self):
|
|
self.squares = get_squares().values()
|
|
|
|
def time_axis_0(self):
|
|
[np.add.reduce(a, axis=0) for a in self.squares]
|
|
|
|
def time_axis_1(self):
|
|
[np.add.reduce(a, axis=1) for a in self.squares]
|
|
|
|
|
|
class AddReduceSeparate(Benchmark):
|
|
params = [[0, 1], TYPES1]
|
|
param_names = ['axis', 'type']
|
|
|
|
def setup(self, axis, typename):
|
|
self.a = get_squares()[typename]
|
|
|
|
def time_reduce(self, axis, typename):
|
|
np.add.reduce(self.a, axis=axis)
|
|
|
|
|
|
class AnyAll(Benchmark):
|
|
def setup(self):
|
|
# avoid np.zeros's lazy allocation that would
|
|
# cause page faults during benchmark
|
|
self.zeros = np.full(100000, 0, bool)
|
|
self.ones = np.full(100000, 1, bool)
|
|
|
|
def time_all_fast(self):
|
|
self.zeros.all()
|
|
|
|
def time_all_slow(self):
|
|
self.ones.all()
|
|
|
|
def time_any_fast(self):
|
|
self.ones.any()
|
|
|
|
def time_any_slow(self):
|
|
self.zeros.any()
|
|
|
|
|
|
class MinMax(Benchmark):
|
|
params = [np.float32, np.float64, np.intp]
|
|
param_names = ['dtype']
|
|
|
|
def setup(self, dtype):
|
|
self.d = np.ones(20000, dtype=dtype)
|
|
|
|
def time_min(self, dtype):
|
|
np.min(self.d)
|
|
|
|
def time_max(self, dtype):
|
|
np.max(self.d)
|
|
|
|
|
|
class SmallReduction(Benchmark):
|
|
def setup(self):
|
|
self.d = np.ones(100, dtype=np.float32)
|
|
|
|
def time_small(self):
|
|
np.sum(self.d)
|