我们从Python开源项目中，提取了以下23个代码示例，用于说明如何使用scipy.stats.norm.rvs()。
def _generatePos(self, lenBackground, lenSubstring, additionalInfo): from scipy.stats import norm center = (lenBackground-lenSubstring)/2.0 validPos = False totalTries = 0 while (validPos == False): sampledPos = int(norm.rvs(loc=center+self.offsetFromCenter, scale=self.stdInBp)) totalTries += 1 if (sampledPos > 0 and sampledPos < (lenBackground-lenSubstring)): validPos = True if (totalTries%10 == 0 and totalTries > 0): print("Warning: made "+str(totalTries)+" attempts at sampling" +" a position with lenBackground "+str(lenBackground) +" and center "+str(center)+" and offset " +str(self.offsetFromCenter)) return sampledPos
def test_aic_fail_no_posterior(): d = norm.rvs(size=1000) c = ChainConsumer() c.add_chain(d, num_eff_data_points=1000, num_free_params=1) aics = c.comparison.aic() assert len(aics) == 1 assert aics[0] is None
def test_aic_fail_no_data_points(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1) aics = c.comparison.aic() assert len(aics) == 1 assert aics[0] is None
def test_aic_fail_no_num_params(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_eff_data_points=1000) aics = c.comparison.aic() assert len(aics) == 1 assert aics[0] is None
def test_aic_0(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=1000) aics = c.comparison.aic() assert len(aics) == 1 assert aics[0] == 0
def test_aic_posterior_dependence(): d = norm.rvs(size=1000) p = norm.logpdf(d) p2 = norm.logpdf(d, scale=2) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=1000) c.add_chain(d, posterior=p2, num_free_params=1, num_eff_data_points=1000) aics = c.comparison.aic() assert len(aics) == 2 assert aics[0] == 0 expected = 2 * np.log(2) assert np.isclose(aics[1], expected, atol=1e-3)
def test_aic_data_dependence(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=1000) c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=500) aics = c.comparison.aic() assert len(aics) == 2 assert aics[0] == 0 expected = (2.0 * 1 * 2 / (500 - 1 - 1)) - (2.0 * 1 * 2 / (1000 - 1 - 1)) assert np.isclose(aics[1], expected, atol=1e-3)
def test_bic_fail_no_posterior(): d = norm.rvs(size=1000) c = ChainConsumer() c.add_chain(d, num_eff_data_points=1000, num_free_params=1) bics = c.comparison.bic() assert len(bics) == 1 assert bics[0] is None
def test_bic_fail_no_data_points(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1) bics = c.comparison.bic() assert len(bics) == 1 assert bics[0] is None
def test_bic_fail_no_num_params(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_eff_data_points=1000) bics = c.comparison.bic() assert len(bics) == 1 assert bics[0] is None
def test_bic_0(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=1000) bics = c.comparison.bic() assert len(bics) == 1 assert bics[0] == 0
def test_bic_parameter_dependence(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=1000) c.add_chain(d, posterior=p, num_free_params=2, num_eff_data_points=1000) bics = c.comparison.bic() assert len(bics) == 2 assert bics[0] == 0 expected = np.log(1000) assert np.isclose(bics[1], expected, atol=1e-3)
def test_bic_data_dependence(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=1000) c.add_chain(d, posterior=p, num_free_params=1, num_eff_data_points=500) bics = c.comparison.bic() assert len(bics) == 2 assert bics[1] == 0 expected = np.log(1000) - np.log(500) assert np.isclose(bics[0], expected, atol=1e-3)
def test_bic_data_dependence2(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p, num_free_params=2, num_eff_data_points=1000) c.add_chain(d, posterior=p, num_free_params=3, num_eff_data_points=500) bics = c.comparison.bic() assert len(bics) == 2 assert bics[0] == 0 expected = 3 * np.log(500) - 2 * np.log(1000) assert np.isclose(bics[1], expected, atol=1e-3)
def test_dic_fail_no_posterior(): d = norm.rvs(size=1000) c = ChainConsumer() c.add_chain(d, num_eff_data_points=1000, num_free_params=1) dics = c.comparison.dic() assert len(dics) == 1 assert dics[0] is None
def test_dic_0(): d = norm.rvs(size=1000) p = norm.logpdf(d) c = ChainConsumer() c.add_chain(d, posterior=p) dics = c.comparison.dic() assert len(dics) == 1 assert dics[0] == 0
def get_position(self): dt = int(time.time()) - self.ts_now is_moving = (random.random() > 0.40) if is_moving: self.x += norm.rvs(scale=self.delta**2*dt) self.y += norm.rvs(scale=self.delta**2*dt) self.ts_now += dt return { 'ts': self.ts_now, 'x': self.x, 'y': self.y, 'port_id': BROKER_CLIENT_ID }
def signal_generator(center=[70, 0], width=1): while True: yield norm.rvs(loc=center, scale=width)
def test_densratio_1d(self): x = norm.rvs(size = 200, loc = 0, scale = 1./8, random_state = 71) y = norm.rvs(size = 200, loc = 0, scale = 1./2, random_state = 71) result = densratio(x, y) self.assertIsNotNone(result) density_ratio = result.compute_density_ratio(linspace(-1, 3)) # print(density_ratio)
def test_densratio_2d(self): x = multivariate_normal.rvs(size = 300, mean = [1, 1], cov = [[1./8, 0], [0, 2]], random_state = 71) y = multivariate_normal.rvs(size = 300, mean = [1, 1], cov = [[1./2, 0], [0, 2]], random_state = 71) result = densratio(x, y) self.assertIsNotNone(result)
def test_densratio_dimension_error(self): x = norm.rvs(size = 200, loc = 0, scale = 1./8, random_state = 71) y = multivariate_normal.rvs(size = 300, mean = [1, 1], cov = [[1./2, 0], [0, 2]], random_state = 71) with self.assertRaises(ValueError): densratio(x, y)
def plot_mi_demo(): np.random.seed(0) # to reproduce the data later on pylab.clf() pylab.figure(num=None, figsize=(8, 8)) x = np.arange(0, 10, 0.2) pylab.subplot(221) y = 0.5 * x + norm.rvs(1, scale=.01, size=len(x)) _plot_mi_func(x, y) pylab.subplot(222) y = 0.5 * x + norm.rvs(1, scale=.1, size=len(x)) _plot_mi_func(x, y) pylab.subplot(223) y = 0.5 * x + norm.rvs(1, scale=1, size=len(x)) _plot_mi_func(x, y) pylab.subplot(224) y = norm.rvs(1, scale=10, size=len(x)) _plot_mi_func(x, y) pylab.autoscale(tight=True) pylab.grid(True) filename = "mi_demo_1.png" pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight") pylab.clf() pylab.figure(num=None, figsize=(8, 8)) x = np.arange(-5, 5, 0.2) pylab.subplot(221) y = 0.5 * x ** 2 + norm.rvs(1, scale=.01, size=len(x)) _plot_mi_func(x, y) pylab.subplot(222) y = 0.5 * x ** 2 + norm.rvs(1, scale=.1, size=len(x)) _plot_mi_func(x, y) pylab.subplot(223) y = 0.5 * x ** 2 + norm.rvs(1, scale=1, size=len(x)) _plot_mi_func(x, y) pylab.subplot(224) y = 0.5 * x ** 2 + norm.rvs(1, scale=10, size=len(x)) _plot_mi_func(x, y) pylab.autoscale(tight=True) pylab.grid(True) filename = "mi_demo_2.png" pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
def plot_correlation_demo(): np.random.seed(0) # to reproduce the data later on pylab.clf() pylab.figure(num=None, figsize=(8, 8)) x = np.arange(0, 10, 0.2) pylab.subplot(221) y = 0.5 * x + norm.rvs(1, scale=.01, size=len(x)) _plot_correlation_func(x, y) pylab.subplot(222) y = 0.5 * x + norm.rvs(1, scale=.1, size=len(x)) _plot_correlation_func(x, y) pylab.subplot(223) y = 0.5 * x + norm.rvs(1, scale=1, size=len(x)) _plot_correlation_func(x, y) pylab.subplot(224) y = norm.rvs(1, scale=10, size=len(x)) _plot_correlation_func(x, y) pylab.autoscale(tight=True) pylab.grid(True) filename = "corr_demo_1.png" pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight") pylab.clf() pylab.figure(num=None, figsize=(8, 8)) x = np.arange(-5, 5, 0.2) pylab.subplot(221) y = 0.5 * x ** 2 + norm.rvs(1, scale=.01, size=len(x)) _plot_correlation_func(x, y) pylab.subplot(222) y = 0.5 * x ** 2 + norm.rvs(1, scale=.1, size=len(x)) _plot_correlation_func(x, y) pylab.subplot(223) y = 0.5 * x ** 2 + norm.rvs(1, scale=1, size=len(x)) _plot_correlation_func(x, y) pylab.subplot(224) y = 0.5 * x ** 2 + norm.rvs(1, scale=10, size=len(x)) _plot_correlation_func(x, y) pylab.autoscale(tight=True) pylab.grid(True) filename = "corr_demo_2.png" pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
