def random_branch(romes, rho, m, mean_hirings, siret='.', nb='.'):
return Branch(rd.choice(romes),
rd.choice(rho),
m[rd.choice(range(len(m)))],
rd.chisquare(mean_hirings),
siret=siret,
nb=nb)
def random_modi(size):
x = randn(size)
range_y = np.exp2(0.3 * x**3 + np.sqrt(abs(x)))
range_y[np.where(range_y>1.3)] = 0.23 * \
chisquare(13,np.where(range_y>1.3)[0].size)
factor_y = range_y / 3.0 * randn(size) * 0.1
return (factor_y)
def rwish(shape, scale, samples=1):
'''
Generate random samples from Wishart distribution. Based on rwish() in the MCMCpack R package.
That package is licensed under GPL-v3 and can be found:
http://cran.r-project.org/web/packages/MCMCpack/index.html
:attr:`shape` is the shape parameter, a real number.
:attr:`scale` is the scale parameter, a square matrix whose dimensions must not be greater than the shape parameter
:attr:`samples` is the number of random samples to return
'''
if len(scale.shape) != 2:
if scale.shape[0] != 1:
raise ValueError('Scale parameter must be a 2-D matrix')
if scale.shape[0] != scale.shape[1]:
raise ValueError('Scale parameter must be a square matrix')
p = scale.shape[0]
if shape < p:
raise ValueError(
'Shape parameter must be equal to or greater than the number of dimensions.'
)
chol = la.cholesky(scale)
result = np.zeros((samples, p, p))
for i in range(samples):
z = np.eye(p)
z = z * np.sqrt(npr.chisquare([x + shape
for x in range(0, -p, -1)], p))
if p > 1:
pseq = range(1, p)
z[np.triu_indices(p, 1)] = np.random.normal(size=p * (p - 1) / 2)
a = chol.dot(z)
result[i] = a.dot(a.T)
return result
def chisquareAverage(numSamples=100000):
runningSum = 0
for _ in range(numSamples):
runningSum += chisquare(2)
return runningSum/numSamples
def generate(self, S) -> np.ndarray:
gaussian = rd.multivariate_normal(np.zeros(N), self.cov, S)
chi2 = rd.chisquare(self.nu, (S, 1))
LogScenarios = gaussian / np.sqrt(self.nu / chi2) + np.array(self.mean)
return np.concatenate(((1 + self.r) * np.ones(
(S, 1)), np.exp(LogScenarios)),
axis=1)
def MiChisquare(dof):
"""ChiSquare Distribution Function
dof: Degrees of Freedom"""
global manflag
if not manflag:
setManual()
return np.chisquare(dof,1)
def chi2_mean_std(self, mean=1., std=0.1):
'''
Chi-squared random variable with given mean and standard deviation.
'''
scale = 2. * mean / std
nu = mean * scale
return npr.chisquare(nu) / scale
def rwish(shape, scale, samples=1):
'''
Generate random samples from Wishart distribution. Based on rwish() in the MCMCpack R package.
That package is licensed under GPL-v3 and can be found:
http://cran.r-project.org/web/packages/MCMCpack/index.html
:attr:`shape` is the shape parameter, a real number.
:attr:`scale` is the scale parameter, a square matrix whose dimensions must not be greater than the shape parameter
:attr:`samples` is the number of random samples to return
'''
if len(scale.shape) != 2:
if scale.shape[0] != 1:
raise ValueError('Scale parameter must be a 2-D matrix')
if scale.shape[0] != scale.shape[1]:
raise ValueError('Scale parameter must be a square matrix')
p = scale.shape[0]
if shape < p:
raise ValueError('Shape parameter must be equal to or greater than the number of dimensions.')
chol = la.cholesky(scale)
result = np.zeros((samples,p,p))
for i in range(samples):
z = np.eye(p)
z = z * np.sqrt(npr.chisquare([x+shape for x in range(0,-p,-1)],p))
if p > 1:
pseq = range(1,p)
z[np.triu_indices(p,1)] = np.random.normal(size=p*(p-1)/2)
a = chol.dot(z)
result[i] = a.dot(a.T)
return result
def get_dist_num(args):
dist = args[0]
for i in range(len(args[1:])):
args[i + 1] = float(args[1:][i])
if dist == 'EXP':
return exponential(args[1])
elif dist == 'NOR':
return normal(loc=args[1],
scale=args[2]) # loc = média , scale = desvio
elif dist == 'TRI':
return triangular(args[1], args[2], args[3])
elif dist == 'UNI':
return uniform(low=args[1], high=args[2])
elif dist == 'BET':
return beta(args[1], args[2])
elif dist == 'WEI':
return weibull(args[1])
elif dist == 'CAU': # CAU: Cauchy
return 0
elif dist == 'CHI':
return chisquare(args[1])
elif dist == 'ERL': # ERL: Erlang
return 0
elif dist == 'GAM':
return gamma(args[1], scale=args[2])
elif dist == 'LOG':
return lognormal(mean=args[1], sigma=args[2])
elif dist == 'PAR':
return pareto(args[1])
elif dist == 'STU':
return standard_t(args[1])
def estimate_sigma_e(s_e, y, X, b, Z, u, tau_e, Tau_e, family_indices):
"""
Updates the estimate for sigma_e
"""
y_copy = y.copy()
X_copy = X.copy()
Z_copy = Z.copy()
S_t = 0
for family_ind in family_indices:
# Calculate the S_t for every group m separately
y_subset = np.squeeze(y_copy[family_ind])
X_subset = X_copy[family_ind]
Z_subset = Z_copy[family_ind]
s_e_i = s_e[family_ind][0]
S_t_m = calculate_sum(s_e_i, y_subset, X_subset, b, Z_subset, u)
S_t = S_t + S_t_m
nominator = (tau_e * Tau_e) + S_t
df = tau_e + len(y)
sigma_estimate = nominator / chisquare(df=df)
return sigma_estimate
def estimate_s_e(sigma_e, nu_e, y, X, b, Z, u, family_indices):
"""
Generates s_e for each i, where i = {1, ..., m} and m is the number of groups. In this case m is the number of families.
"""
# Initialize s_e
s_e = np.zeros(len(y))
for ind in family_indices:
y_copy = np.squeeze(y.copy())
X_copy = X.copy()
Z_copy = Z.copy()
# Calculate the S_e for every other observation, except belonging to this group
y_subset = y_copy[ind]
X_subset = X_copy[ind, :]
Z_subset = Z_copy[ind, :]
S_e = calculate_S_e(sigma_e, nu_e, y_subset, X_subset, b, Z_subset, u)
# Calculate the estimate for the family i
df = nu_e + len(ind)
s_e_i = chisquare(df) / S_e
# Update the s_e for ith family
s_e[ind] = s_e_i
return s_e
def generate_Wishart(n, V):
"""
Generate a sample from Wishart density
Parameters
----------
n: float,
the number of degrees of freedom of the Wishart density
V: array of shape (n,n)
the scale matrix of the Wishart density
Returns
-------
W: array of shape (n,n)
the draw from Wishart density
"""
icv = cholesky(V)
p = V.shape[0]
A = nr.randn(p, p)
for i in range(p):
A[i, i:] = 0
A[i, i] = np.sqrt(nr.chisquare(n - i))
R = np.dot(icv, A)
W = np.dot(R, R.T)
return W
def distribution():
sample_size = 500
rn1 = npr.standard_normal(sample_size)
rn2 = npr.normal(100, 20, sample_size)
rn3 = npr.chisquare(df=0.5, size=sample_size)
rn4 = npr.poisson(lam=1.0, size=sample_size)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2,
ncols=2,
figsize=(10, 8))
ax1.hist(rn1, bins=25, stacked=True)
ax1.set_title('Standard normal')
ax1.set_ylabel('frequency')
ax2.hist(rn2, bins=25)
ax2.set_title('Normal 100,20')
ax3.hist(rn3, bins=25)
ax3.set_title('Chi squared')
ax3.set_ylabel("frequency")
ax4.hist(rn4, bins=25)
ax4.set_title('Poisson')
plt.show()
def generate_Wishart(n, V):
"""
Generate a sample from Wishart density
Parameters
----------
n: float,
the number of degrees of freedom of the Wishart density
V: array of shape (n,n)
the scale matrix of the Wishart density
Returns
-------
W: array of shape (n,n)
the draw from Wishart density
"""
icv = cholesky(V)
p = V.shape[0]
A = nr.randn(p, p)
for i in range(p):
A[i, i:] = 0
A[i, i] = np.sqrt(nr.chisquare(n - i))
R = np.dot(icv, A)
W = np.dot(R, R.T)
return W
def generateStudentTScenarios(NbScenarios=1000,
nu=3,
start=None,
end=None,
seed=None):
"""
Generates random scenarios based on a multivariate 'student' t distribution of the log returns.
- NbScenarios: int - Number of scenarios to compute
- start/end: period - Period on which to compute the
variance-covariance matrix
- seed: int - Seed for random generation
"""
LocalData = Data[start:end]
LocalReturns = (LocalData / LocalData.shift() - 1)[1:]
MeanLocalReturns = LocalReturns.mean()
CovLocalReturns = LocalReturns.cov()
if seed is not None:
rd.seed(seed)
gaussian = rd.multivariate_normal(np.zeros(len(Data.columns)),
CovLocalReturns, NbScenarios)
chi2 = rd.chisquare(nu, (NbScenarios, 1))
scenarios = gaussian / np.sqrt(nu / chi2) + np.array(MeanLocalReturns)
probas = np.ones(NbScenarios) / NbScenarios
return scenarios, probas
def estimate_sigma_u(s_u, y, X, b, Z, u, tau_u, Tau_u):
covariance_u = calculate_uAu(Z.copy(), u.copy())
nominator = (tau_u*Tau_u + s_u*covariance_u)
df = tau_u + Z.shape[1]
updated_sigma_u = nominator / chisquare(df=df)
return updated_sigma_u
def estimate_s_u(Z, u, sigma_u, nu_u):
"""
Generates s_u for each familial random effect.
"""
df = nu_u + Z.shape[1]
scaler = calculate_covariance_u(Z.copy(), u.copy(), sigma_u, nu_u)
s_u_estimate = chisquare(df) / scaler
return s_u_estimate
def simulateCopula(simulations=10, type=str('g'), rho=float, lamda=tuple, tDof=4, basketSize=5, useGPU=False):
result = []
"""
$\tau = F^{-1}(u) = -\frac{log(1-u)}{\lambda}$
"""
print 'simulating t distribution' if type == 't' else 'simulating gaussian dist'
for z in xrange(0, simulations):
# for the t distribution we use the same method but
# sample from the chisquared distribution
# if GPU is enabled, hand over to GPU to provide random number sample
if useGPU and type == 'g':
z1, z2, z3, z4, z5 = rng.getPseudoRandomNumbers_Standard_cuda(basketSize)
else:
z1, z2, z3, z4, z5 = random.chisquare(tDof, size=basketSize) if type == 't' else random.normal(size=5)
# z1, z2, z3, z4, z5 = chi2.rvs(1, size=5) if type == 't' else random.normal(size=5)
x1 = z1
# using factorised copula procedure
# $A_i = w_iZ + \sqrt{1-w{^2}{_i}\Epsilon_i $
x2, x3, x4, x5 = [z1 * rho + sqrt(1 - square(rho)) * zn for zn in [z2, z3, z4, z5]]
# converting to normal variables from t or normal distribution successfully
# via cdf of relevant distribution
if type == 't':
u1, u2, u3, u4, u5 = [t.cdf(x, 1) for x in [x1, x2, x3, x4, x5]]
else:
u1, u2, u3, u4, u5 = [norm.cdf(x) for x in [x1, x2, x3, x4, x5]]
u = [u1, u2, u3, u4, u5]
# $\tau_i = -\frac{-log(1-u)}{\lambda_i} $
tau1, tau2, tau3, tau4, tau5 = [-log(1 - u) / lamda[index] for index, u in enumerate(u)]
result.append({'z1': z1,
'z2': z2,
'z3': z3,
'z4': z4,
'z5': z5,
'x1': x1,
'x2': x2,
'x3': x3,
'x4': x4,
'x5': x5,
'u1': u1,
'u2': u2,
'u3': u3,
'u4': u4,
'u5': u5,
'tau1': tau1,
'tau2': tau2,
'tau3': tau3,
'tau4': tau4,
'tau5': tau5,
})
return DataFrame(result)
def SigmaPrior():
Lambda = 0.2
m = 5
DegreeOfFreedom = n_obs + m - 1
sigma_sq_inv = rand.chisquare(DegreeOfFreedom)
sigma_sq = dict()
sigma_sq['Value'] = float(m * Lambda) / sigma_sq_inv
sigma_sq['Lambda'] = Lambda
sigma_sq['m'] = m
return sigma_sq
def chi2_mean_std(self,mean=1.,std=0.1):
"""
Chi-squared random variable with given mean and standard deviation.
:param mean:
:param std:
:return:
"""
scale = 2.*mean/std
nu = mean*scale
return npr.chisquare(nu)/scale
def gen_logit(N):
Data=[]
for n in range(N):
x1=nprd.normal()*1.414+1
x2=nprd.chisquare(2)
d_star=beta_0+beta_1*x1+beta_2*x2
p_star=Logistic(d_star)
d=(1 if nprd.uniform()<p_star else 0)
Data.append((d,x1,x2))
return Data
def time_to_mutation_rate(tree):
if not hasattr(GC, "NUMPY_SEEDED"):
from numpy.random import seed as numpy_seed
numpy_seed(seed=GC.random_number_seed)
GC.random_number_seed += 1
GC.NUMPY_SEEDED = True
t = read_tree_newick(tree)
for node in t.traverse_preorder():
if node.edge_length is not None:
node.edge_length *= chisquare(df=GC.tree_rate_df)
return str(t)
def UpdateSigma():
Alpha = Parameters.Alpha['Value']
Lambda = Parameters.Sigma_Sq['Lambda']
m = Parameters.Sigma_Sq['m']
v = Log_H - Alpha[0] - Alpha[1] * Log_Lag_H
Numerator = m * Lambda + np.sum(np.square(v))
Chi2Draw = rand.chisquare(df=m + len(v) - 1)
NewValue = Numerator / Chi2Draw
NewSigma_Sq = Parameters.Sigma_Sq.copy()
NewSigma_Sq['Value'] = NewValue
return NewSigma_Sq
def empiricalEW(i, w, p, numSamples=100000):
runningSum = 0
for _ in range(numSamples):
x = chisquare(2)
eigPart = (p*(1-p)*x)/(2*w)
runningSum += log(eigPart + 1, 2)
return runningSum/numSamples
def _loop_gain_ll(self, cur_select, cur_update):
"""循环获取经纬度的信息"""
failure = 0
while cur_select.rownumber < cur_select.rowcount:
try:
sample_info = cur_select.fetchone()
self._gain_ll(sample_info, cur_update)
i = chisquare(0.5)
time.sleep(i)
except:
failure += 1
print(u'经纬度获取失败，累计获取失败样本：%d 条' % failure)
finally:
self.conn.commit()
def PlotRandomSVD(nrow=51,ncol=51,std=1,dist='normal'):
if dist=='normal':
Q = random.normal(0,std,nrow * ncol).reshape(nrow,ncol)
elif dist=='chisq':
Q = random.chisquare(std,nrow * ncol).reshape(nrow,ncol)
else: raise ValueError(dist + " Unknown dist choice")
U,S,V = np.linalg.svd(np.matrix(Q),full_matrices=True)
plt.plot(np.arange(S.shape[0]),S)
plt.xlabel('Descending Sorted Order')
plt.ylabel('%s Random Eigen Values'%(dist.upper()))
def PlotRandomSVD(nrow=51, ncol=51, std=1, dist='normal'):
if dist == 'normal':
Q = random.normal(0, std, nrow * ncol).reshape(nrow, ncol)
elif dist == 'chisq':
Q = random.chisquare(std, nrow * ncol).reshape(nrow, ncol)
else:
raise ValueError(dist + " Unknown dist choice")
U, S, V = np.linalg.svd(np.matrix(Q), full_matrices=True)
plt.plot(np.arange(S.shape[0]), S)
plt.xlabel('Descending Sorted Order')
plt.ylabel('%s Random Eigen Values' % (dist.upper()))
def random_numbers():
print(npr.rand(10))
print(npr.rand(5,5))
#interval a to b, 5 to 10
a = 5; b=10
print(npr.rand(10)*(b-a) + a)
sample_size = 500
rn1 = npr.rand(sample_size, 3)
rn2 = npr.randint(0, 10, sample_size)
rn3 = npr.sample(size = sample_size)
a = [0, 25, 50, 75, 100]
rn4 = npr.choice(a, size = sample_size)
print("rn1 : %s" % rn1)
print("rn2 : %s" % rn2)
print("rn3 : %s" % rn3)
print("rn4 : %s" % rn4)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(7, 7))
ax1.hist(rn1, bins=25, stacked=True) ; ax1.set_title("rand") ; ax1.set_ylabel("frequency") ; ax1.grid(False)
ax2.hist(rn2, bins=25) ; ax2.set_title("randint") ; ax2.grid(True)
ax3.hist(rn3, bins=25) ; ax3.set_title("sample") ; ax3.set_ylabel("frequency")
ax4.hist(rn4, bins=25) ; ax4.set_title("choice") ; ax4.grid(True)
plt.show()
#distributions
sample_size = 500
rn1 = npr.standard_normal(sample_size)
rn2 = npr.normal(100, 20, sample_size)
rn3 = npr.chisquare(df=0.5, size=sample_size)
rn4 = npr.poisson(lam=1.0, size=sample_size)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10,10))
ax1.hist(rn1, bins=25) ; ax1.set_title("standard normal") ; ax1.set_ylabel("frequency") ; ax1.grid(True)
ax2.hist(rn2, bins=25) ; ax2.set_title("normal(100, 20)") ; ax2.set_xlabel("hi") ; ax2.grid(True)
ax3.hist(rn3) ; ax3.set_title("chi square") ; ax3.set_ylabel("frequency") ; ax3.grid(True)
ax4.hist(rn4, bins=100) ; ax4.set_title("Poisson")
plt.show()
pass
def rvs(self):
# Random normal variates for off-diagonal elements
n_tril = self.dim * (self.dim - 1) // 2
covariances = npr.normal(size=n_tril).reshape((n_tril,))
# Random chi-square variates for diagonal elements
variances = (np.r_[[npr.chisquare(self.nu - (i + 1) + 1, size=1)**0.5
for i in range(self.dim)]].reshape((self.dim,)).T)
A = np.zeros((self.dim, self.dim))
# Input the covariances
tril_idx = np.tril_indices(self.dim, k=-1)
A[tril_idx] = covariances
# Input the variances
diag_idx = np.diag_indices(self.dim)
A[diag_idx] = variances
T = np.dot(self.psi_chol, A)
return np.dot(T, T.T)
def distribute(args):
n = args.s
feats = args.f
if n >= 10**6:
csv_name = f"{int(n/10**6)}M"
else:
csv_name = f"{int(n/10**3)}K"
base = np.random.normal(loc=.5, scale=.5, size=n)
correlated = []
epsilons = [
np.random.normal(loc=.5, scale=.5, size=n) for _ in range(feats)
]
labels = list(string.ascii_lowercase.upper()[0:10])
uniform = random.uniform(size=(feats, n))
normal = random.normal(loc=.5, scale=5, size=(feats, n))
chisquare = random.chisquare(df=feats, size=(feats, n))
for index, i in enumerate(epsilons):
if index == 0:
correlated.append(base)
else:
correlated.append(base + random.uniform(0.5, 1) * i)
for index, x in enumerate(correlated):
correlated[index] = (x - min(x)) / (max(x) - min(x))
for index, x in enumerate(normal):
normal[index] = (x - min(x)) / (max(x) - min(x))
for index, x in enumerate(chisquare):
chisquare[index] = (x - min(x)) / (max(x) - min(x))
uniform_df = pd.DataFrame(data=uniform.T, columns=labels[0:feats])
normal_df = pd.DataFrame(data=normal.T, columns=labels[0:feats])
chisquare_df = pd.DataFrame(data=chisquare.T, columns=labels[0:feats])
correlated_df = pd.DataFrame(data=np.array(correlated).T,
columns=labels[0:feats])
uniform_df.to_csv(f"{csv_name}x{feats}_uniform.csv", index=False)
normal_df.to_csv(f"{csv_name}x{feats}_normal.csv", index=False)
chisquare_df.to_csv(f"{csv_name}x{feats}_chisquare.csv", index=False)
correlated_df.to_csv(f"{csv_name}x{feats}_correlated.csv", index=False)
def generate_Wishart(n,V):
"""
Generate a sample from Wishart
Parameters
----------
n (scalar) = the number of degrees of freedom (dofs)
V = array of shape (n,n) the scale matrix
Returns
-------
W: array of shape (n,n): the Wishart draw
"""
from numpy.linalg import cholesky
L = cholesky(V)
p = V.shape[0]
A = nr.randn(p,p)
a = np.array([np.sqrt(nr.chisquare(n-i)) for i in range(p)])
for i in range(p):
A[i,i:] = 0
A[i,i] = a[i]
R = np.dot(L,A)
W = np.dot(R,R.T)
return W
def rvs(self, size=1):
# Random normal variates for off-diagonal elements
n_tril = self.dim * (self.dim - 1) // 2
covariances = npr.normal(size=n_tril).reshape((n_tril, ))
# Random chi-square variates for diagonal elements
variances = (np.r_[[
npr.chisquare(self.nu - (i + 1) + 1, size=1)**0.5
for i in range(self.dim)
]].reshape((self.dim, )).T)
A = np.zeros((self.dim, self.dim))
# Input the covariances
tril_idx = np.tril_indices(self.dim, k=-1)
A[tril_idx] = covariances
# Input the variances
diag_idx = np.diag_indices(self.dim)
A[diag_idx] = variances
eye = np.eye(self.dim)
L, lower = sc.linalg.cho_factor(self.psi, lower=True)
inv_scale = sc.linalg.cho_solve((L, lower), eye)
C = sc.linalg.cholesky(inv_scale, lower=True)
trtrs = get_lapack_funcs(('trtrs'), (A, ))
T = np.dot(C, A)
if self.dim > 1:
T, _ = trtrs(T, eye, lower=True)
else:
T = 1. / T
return np.dot(T.T, T)
ax3.hist(rn3, bins=25)
ax3.set_title('sample')
ax3.set_ylabel('frequency')
ax3.grid(True)
ax4.hist(rn4, bins=25)
ax4.set_title('choice')
ax4.grid(True)
plt.show()
## Generating Random Numbers using distributions.
# A few select functions below from numpy random library
sample_size = 500
rn1 = npr.standard_normal(sample_size) # standard normal distribution
rn2 = npr.normal(100, 20, sample_size) # normal distribution
rn3 = npr.chisquare(df=0.5, size=sample_size) # chi square distribution
rn4 = npr.poisson(lam=1.0, size=sample_size) # Poisson distribution
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(7, 7))
ax1.hist(rn1, bins=25)
ax1.set_title('standard normal')
ax1.set_ylabel('frequency')
ax1.grid(True)
ax2.hist(rn2, bins=25)
ax2.set_title('normal (100,20)')
ax2.grid(True)
ax3.hist(rn3, bins=25)
ax3.set_title('chi square')
ax3.set_ylabel('frequency')
ax3.grid(True)
def _numpy(self, loc=0.0, scale=1.0, size=(1,)):
return lambda: nr.chisquare(df=self.df, size=size) * scale + loc
# chi_square_distribution # used as a basis to verify the hypothesis # It has two parameters # df - (degree of freedom) # size - the shape of the returned array # Draw out a sample for chi squared distribution with degree of freedom 2 with size 2x3 from numpy import random x = random.chisquare(df=2, size=(2, 3)) print(x) # visualization of chi-square distribution # from numpy import random import matplotlib.pyplot as plt import seaborn as sns sns.distplot(random.chisquare(df=1, size=1000), hist=False) plt.show()
import numpy.random as rnd
from matplotlib import pyplot as plt
labels = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30
]
rnd01 = rnd.normal(0, 2, 30)
rnd02 = rnd.chisquare(1, 30)
rnd03 = rnd.exponential(1, 30)
plt.subplot(1, 3, 1)
plt.scatter(x=labels, y=rnd01)
plt.xlabel('normal')
plt.subplot(1, 3, 2)
plt.scatter(x=labels, y=rnd02)
plt.xlabel('chisquare')
plt.subplot(1, 3, 3)
plt.scatter(x=labels, y=rnd03)
plt.xlabel('exponential')
plt.show()
def np_chi_square_distribution():
x = random.chisquare(df=2, size=(2, 3))
print(x)
sns.distplot(random.chisquare(df=1, size=1000), hist=False)
plt.show()
def rstudent(mu, lam, alpha):
X = R.chisquare(alpha, mu.shape)
Z = R.standard_normal(mu.shape)
return mu + Z * sqrt(alpha / X) / sqrt(lam)
# normalizedH = h/sqrt(n)
e1 = np.linalg.eig(normalizedC)
# e2 = np.linalg.eig(normalizedH)
# print e1[0]
# print e2[0]
# x1 = sorted(np.multiply(e1[0], np.conjugate(e1[0])))
# x2 = sorted(np.multiply(e2[0], np.conjugate(e2[0])))
x1 = np.multiply(e1[0], np.conjugate(e1[0]))
# x2 = np.multiply(e2[0], np.conjugate(e2[0]))
# print x1
x3 = chisquare(2, int((n-1)/2))
# print x3.shape
x3 = sorted(np.repeat(x3, 2))
# print x3
pl.plot(x1[:-1], "b-")
# pl.plot(x2[:-1], "r-")
# pl.plot(x3, "g-")
pl.show()
sys.exit()
if False:
def f(x, n):
"""calculation of chi-squared"""
return ((exp(-x/2))*x**((n/2)-1))/((2**(n/2))*sp.gamma(n/2))
def Prob(chi, n):
"""return probability that X**2> definit limit"""
# return 1-integrate.quad(lambda x: f(x, n), 0, alpha)[0]
return st.chi2.sf(chi, n)
# Main
N = 10000 # number of repetitions
n = 10. # degrees of freedome
mu = 130 # mean
sigma = sqrt(n) # mean derivation
x_arr = linspace(0, 50, 1000, endpoint=False) # an array
chi = chisquare(n, N) # Random distribution chi-squared
#Draw function
plt.figure(1, figsize=[15, 8])
#plt.subplot(111)
#plt.hist(chi, bins=300, label='datos', normed=True)
#plt.plot(x_arr, f(x_arr, n), '-', color='r', label='funciona chi-quadrado')
#plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.)
plt.subplot(111)
plt.axis([0, 50, 0, 0.16])
plt.hist(distribucion(chiqua, mu, sigma, n, N), bins=100, label='datos', normed=True)
plt.plot(x_arr, f(x_arr, n), '-', color='r', label='funciona chi-quadrado')
#plt.plot(x_arr, f(x_arr, n*3.5), '-', color='y', label='nueva aproximacion de funciona chi-quadrado')
plt.legend(loc=1)
# chi square distribution is used as a basis to verify hypothesis. # it has two parameters # df - (degree of freedom). # size - shape of returned array. from numpy import random import matplotlib.pyplot as plt import seaborn as sns arr1 = random.chisquare(df=1000, size=10) print(arr1) sns.distplot(arr1, hist=False) plt.show()
ax2.set_title('randint')
ax2.grid(True)
ax3.hist(rn3,bins=25)
ax3.set_title('sample')
ax3.set_ylabel('frequency')
ax3.grid(True)
ax4.hist(rn4,bins=25)
ax4.set_title('choice')
ax4.grid(True)
#Visualize random draws from distributions
sample_size=500
rn1=npr.standard_normal(sample_size)
rn2=npr.normal(100,20,sample_size)
rn3=npr.chisquare(df=0.5,size=sample_size)
rn4=npr.poisson(lam=1.0,size=sample_size)
fig,((ax1,ax2),(ax3,ax4))=plt.subplots(nrows=2,ncols=2,figsize=(7,7))
ax1.hist(rn1,bins=25,stacked=True)
ax1.set_title('standard normal')
ax1.set_ylabel('frequency')
ax1.grid(True)
ax2.hist(rn2,bins=25)
ax2.set_title('normal(100,20)')
ax2.grid(True)
ax3.hist(rn3,bins=25)
ax3.set_title('chi square')
ax3.set_ylabel('frequency')
ax3.grid(True)
import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
sample_size = 500
rn1 = npr.standard_normal(sample_size)
rn2 = npr.normal(100, 20, sample_size)
rn3 = npr.chisquare(df=0.5, size=sample_size)
rn4 = npr.poisson(lam=1.0, size=sample_size)
print(rn1)
print(rn2)
print(rn3)
print(rn4)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(7, 7))
ax1.hist(rn1, bins=25)
ax1.set_title('standard normal')
ax1.set_ylabel('frequency')
ax1.grid(True)
ax2.hist(rn2, bins=25)
ax2.set_title('normal(100, 20)')
ax2.grid(True)
ax3.hist(rn3, bins=25)
ax3.set_title('chi square')
ax3.set_ylabel('frequency')
ax3.grid(True)
ax4.hist(rn4, bins=25)
ax4.set_title('Poisson')
ax4.grid(True)
