You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/19 18:19:23 UTC
[GitHub] sxjscience commented on a change in pull request #9129: add tests for distribution generators
sxjscience commented on a change in pull request #9129: add tests for distribution generators
URL: https://github.com/apache/incubator-mxnet/pull/9129#discussion_r157834559
##########
File path: tests/python/unittest/test_random.py
##########
@@ -216,6 +220,91 @@ def test_sample_multinomial():
real_dx[y[i][j]] += 5.0 / rprob[j]
mx.test_utils.assert_almost_equal(real_dx, dx.asnumpy()[i])
+# Test the generators with the chi-square testing
+def test_normal_generator():
+ print("Normal Distribution Test:")
+ ctx = mx.context.current_context()
+ for dtype in ['float16', 'float32', 'float64']:
+ for mu, sigma in [(0.0, 1.0), (1.0, 5.0)]:
+ print("ctx=%s, dtype=%s, Mu=%g, Sigma=%g:" % (ctx, dtype, mu, sigma))
+ buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.norm.ppf(x, mu, sigma), 5)
+ generator_mx = lambda x: mx.nd.random.normal(mu, sigma, shape=x, ctx=ctx, dtype=dtype).asnumpy()
+ verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+
+def test_uniform_generator():
+ print("Uniform Distribution Test:")
+ ctx = mx.context.current_context()
+ for dtype in ['float16', 'float32', 'float64']:
+ for low, high in [(-1.0, 1.0), (1.0, 3.0)]:
+ print("ctx=%s, dtype=%s, Low=%g, High=%g:" % (ctx, dtype, low, high))
+ buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=high - low), 5)
+ generator_mx = lambda x: mx.nd.random.uniform(low, high, shape=x, ctx=ctx, dtype=dtype).asnumpy()
+ verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+
+def test_gamma_generator():
+ print("Gamma Distribution Test:")
+ ctx = mx.context.current_context()
+ for dtype in ['float16', 'float32', 'float64']:
+ for kappa, theta in [(0.5, 1.0), (1.0, 5.0)]:
+ print("ctx=%s, dtype=%s, Shape=%g, Scale=%g:" % (ctx, dtype, kappa, theta))
+ buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.gamma.ppf(x, a=kappa, loc=0, scale=theta), 5)
+ generator_mx = lambda x: mx.nd.random.gamma(kappa, theta, shape=x, ctx=ctx, dtype=dtype).asnumpy()
+ verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+
+def test_exponential_generator():
+ print("Exponential Distribution Test:")
+ ctx = mx.context.current_context()
+ for dtype in ['float16', 'float32', 'float64']:
+ for scale in [0.1, 1.0]:
+ print("ctx=%s, dtype=%s, Scale=%g:" % (ctx, dtype, scale))
+ buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.expon.ppf(x, loc=0, scale=scale), 5)
+ generator_mx = lambda x: mx.nd.random.exponential(scale, shape=x, ctx=ctx, dtype=dtype).asnumpy()
+ verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+
+def test_poisson_generator():
+ print("Poisson Distribution Test:")
+ ctx = mx.context.current_context()
+ for dtype in ['float16', 'float32', 'float64']:
+ for lam in [1, 10]:
+ print("ctx=%s, dtype=%s, Lambda=%d:" % (ctx, dtype, lam))
+ buckets = [(-1.0, lam - 0.5), (lam - 0.5, 2 * lam + 0.5), (2 * lam + 0.5, np.inf)]
+ probs = [ss.poisson.cdf(bucket[1], lam) - ss.poisson.cdf(bucket[0], lam) for bucket in buckets]
+ generator_mx = lambda x: mx.nd.random.poisson(lam, shape=x, ctx=ctx, dtype=dtype).asnumpy()
+ verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+
+def test_negative_binomial_generator():
+ print('Negative Binomial Distribution Test:')
+ ctx = mx.context.current_context()
+ for dtype in ['float16', 'float32', 'float64']:
+ success_num = 2
+ success_prob = 0.2
+ print("ctx=%s, dtype=%s, Success Num=%d:, Success Prob=%g" % (ctx, dtype, success_num, success_prob))
+ buckets = [(-1.0, 2.5), (2.5, 5.5), (5.5, 8.5), (8.5, np.inf)]
+ probs = [ss.nbinom.cdf(bucket[1], success_num, success_prob) -
+ ss.nbinom.cdf(bucket[0], success_num, success_prob) for bucket in buckets]
+ generator_mx = lambda x: mx.nd.random.negative_binomial(success_num, success_prob,
+ shape=x, ctx=ctx, dtype=dtype).asnumpy()
+ verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+ # Also test the Gamm-Poisson Mixture
+ print('Gamm-Poisson Mixture Test:')
+ alpha = 1.0 / success_num
+ mu = (1.0 - success_prob) / success_prob / alpha
+ generator_mx = lambda x: mx.nd.random.generalized_negative_binomial(mu, alpha,
+ shape=x, ctx=ctx, dtype=dtype).asnumpy()
+ verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+
+def test_multinomial_generator():
+ print('Multinomial Distribution Test:')
+ ctx = mx.context.current_context()
+ probs = [0.1, 0.2, 0.3, 0.05, 0.15, 0.2]
+ sample_num = 1000000
+ buckets = list(range(6))
+ for dtype in ['float16', 'float32', 'float64']:
+ print("ctx=%s, dtype=%s" %(ctx, dtype))
+ generator_mx = lambda x: mx.nd.random.multinomial(data=mx.nd.array(np.array(probs), ctx=ctx, type=dtype),
Review comment:
Yes, should be dtype
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services