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
python类rvs()的实例源码
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)
demo_mi.py 文件源码
项目：Building-Machine-Learning-Systems-With-Python-Second-Edition
作者: PacktPublishing
项目源码
文件源码
阅读 25
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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")
demo_corr.py 文件源码
项目：Building-Machine-Learning-Systems-With-Python-Second-Edition
作者: PacktPublishing
项目源码
文件源码
阅读 20
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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")
