1 files changed, 74 insertions, 0 deletions
diff --git a/doc/source/reference/random/performance.py b/doc/source/reference/random/performance.py
new file mode 100644
index 000000000..12cbbc5d3
--- /dev/null
+++ b/doc/source/reference/random/performance.py
@@ -0,0 +1,74 @@
+from collections import OrderedDict
+from timeit import repeat
+
+import numpy as np
+import pandas as pd
+
+from randomgen import MT19937, DSFMT, ThreeFry, PCG64, Xoroshiro128, \
+    Xorshift1024, Philox, Xoshiro256StarStar, Xoshiro512StarStar
+
+PRNGS = [DSFMT, MT19937, Philox, PCG64, ThreeFry, Xoroshiro128, Xorshift1024,
+         Xoshiro256StarStar, Xoshiro512StarStar]
+
+funcs = {'32-bit Unsigned Ints': 'random_uintegers(size=1000000,bits=32)',
+         '64-bit Unsigned Ints': 'random_uintegers(size=1000000,bits=32)',
+         'Uniforms': 'random_sample(size=1000000)',
+         'Complex Normals': 'complex_normal(size=1000000)',
+         'Normals': 'standard_normal(size=1000000)',
+         'Exponentials': 'standard_exponential(size=1000000)',
+         'Gammas': 'standard_gamma(3.0,size=1000000)',
+         'Binomials': 'binomial(9, .1, size=1000000)',
+         'Laplaces': 'laplace(size=1000000)',
+         'Poissons': 'poisson(3.0, size=1000000)', }
+
+setup = """
+from randomgen import {prng}
+rg = {prng}().generator
+"""
+
+test = "rg.{func}"
+table = OrderedDict()
+for prng in PRNGS:
+    print(prng)
+    col = OrderedDict()
+    for key in funcs:
+        t = repeat(test.format(func=funcs[key]),
+                   setup.format(prng=prng().__class__.__name__),
+                   number=1, repeat=3)
+        col[key] = 1000 * min(t)
+    col = pd.Series(col)
+    table[prng().__class__.__name__] = col
+
+npfuncs = OrderedDict()
+npfuncs.update(funcs)
+npfuncs['32-bit Unsigned Ints'] = 'randint(2**32,dtype="uint32",size=1000000)'
+npfuncs['64-bit Unsigned Ints'] = 'tomaxint(size=1000000)'
+del npfuncs['Complex Normals']
+setup = """
+from numpy.random import RandomState
+rg = RandomState()
+"""
+col = {}
+for key in npfuncs:
+    t = repeat(test.format(func=npfuncs[key]),
+               setup.format(prng=prng().__class__.__name__),
+               number=1, repeat=3)
+    col[key] = 1000 * min(t)
+table['NumPy'] = pd.Series(col)
+
+table = pd.DataFrame(table)
+table = table.reindex(table.mean(1).sort_values().index)
+order = np.log(table).mean().sort_values().index
+table = table.T
+table = table.reindex(order)
+table = table.T
+print(table.to_csv(float_format='%0.1f'))
+
+rel = table.loc[:, ['NumPy']].values @ np.ones((1, table.shape[1])) / table
+rel.pop(rel.columns[0])
+rel = rel.T
+rel['Overall'] = np.exp(np.log(rel).mean(1))
+rel *= 100
+rel = np.round(rel)
+rel = rel.T
+print(rel.to_csv(float_format='%0d'))