def plot_like(self):
plt.figure()
xv = np.arange(0,1,0.01)
yin = beta.pdf(xv,self.alp_in,self.bet_in)
yout = beta.pdf(xv,self.alp_out,self.bet_out)
plt.plot(xv,yin)
plt.plot(xv,yout)
def g(x): prob_xx=beta.cdf(x,alpha_param,beta_param)+z if prob_xx>1: #Numerical precision paranoia prob_xx=1 xx=beta.ppf(prob_xx,alpha_param,beta_param) prob_diff=beta.pdf(x,alpha_param,beta_param)-beta.pdf(xx,alpha_param,beta_param) #print ' x=%f, prob_xx=%f, xx=%f, prob_diff=%f'%(x,prob_xx,xx,prob_diff) return(prob_diff)
def beta_proposal(current, var):
alpha_beta_fwd = jmutils.beta_shape(current, var)
proposed = beta.rvs(*alpha_beta_fwd)
fwd_prob = beta.pdf(proposed, *alpha_beta_fwd)
alpha_beta_back = jmutils.beta_shape(proposed, var)
back_prob = beta.pdf(current, *alpha_beta_back)
log_back_fwd = math.log(back_prob / fwd_prob)
return proposed, log_back_fwd
def bin_conj_prior():
# Constants
global bin_data, bin_mean
a, b = 2, 4
m, l = 0, 0
count = 0
u_list = []
domain = np.linspace(beta.ppf(0.01, a, b),
beta.ppf(0.99, a, b), 100)
prior = beta.pdf(domain, a, b)
fig, ax = plt.subplots(1, 1)
# Update
for i in bin_data:
# Go through data
count += 1
if i == 1:
m += 1
else:
l += 1
# Calculate mean
u = (a+m)/(a+m+l+b)
u_list.append(u)
# Calculate posterior
posterior = beta.pdf(domain, a+m, b+l)
# MSE
bin_mean_list = [] # Obtuse way to find MSE, but effective
for i in range(len(u_list)):
bin_mean_list.append(bin_mean)
mse = np.array(u_list) - np.array(bin_mean_list)
# Plot Prior
plt.figure(2)
plt.plot(domain, prior)
plt.title('Binomial - Prior')
plt.xlabel('Number of Observations')
plt.ylabel('PDF')
# Plot Posterior
plt.figure(3)
plt.title('Binomial - Posterior')
plt.xlabel('Number of Observations')
plt.ylabel('PDF')
plt.xlim(0,1)
if count % 5 == 0:
plt.plot(domain, posterior)
# Plot error
plt.figure(4)
plt.plot(mse)
plt.title('Binomial - MSE')
plt.xlabel('Number of Observations')
plt.ylabel('Error')
def priors_plot():
x = sp.linspace(0,1,1000)
y2 = beta.pdf(x,1,2)
y3 = beta.pdf(x,10,20)
y4 = beta.pdf(x,100,200)
plt.plot(x,y2, label = r'$\alpha = 1, \beta = 2$')
plt.plot(x,y3, label = r'$\alpha = 10, \beta = 20$')
plt.plot(x,y4, label = r'$\alpha = 100, \beta = 200$')
plt.legend()
plt.savefig('priors.pdf')
def prob_noisy_vec(given, bayes, noise_type, noise):
if noise_type == "truncnorm":
return tn_pdf_01(given, bayes, noise)
elif noise_type == "beta":
if hasattr(noise, "__len__"):
#return np.array([beta.pdf(g, *jmutils.beta_shape(bayes, n)) for g, n in zip(given, noise)])
return np.array([beta.pdf(g, *mode_beta(bayes, n)) for g, n in zip(given, noise)])
else:
return beta.pdf(given, *jmutils.beta_shape(bayes, noise))
else:
print("Unknown noise type")
sys.exit("prob_noisy_vec: Unknown noise type")
def beta_proposal_old(current, var):
proposed = 0
tries = 0
while (proposed == 0) or (proposed == 1):
proposed = beta.rvs(c * current, c * (1 - current))
tries += 1
if (tries > 1000):
1/0
print("Sampler is jammed")
fwd_prob = beta.pdf(proposed, c * current, c * (1 - current))
back_prob = beta.pdf(current, c * proposed, c * (1 - proposed))
log_back_fwd = math.log(back_prob / fwd_prob)
return proposed, log_back_fwd
def betaconv(res,alpha1, beta1, alpha2, beta2):
#Set support
import numpy as np
from scipy.stats import beta
x=np.arange(0,2.001,res)#0:res:2;
#Individual Beta pdfs
f1 = beta.pdf(x,alpha1,beta1)
f2 = beta.pdf(x,alpha2,beta2)
#Compute convolution
y = np.convolve(f1, f2)
#Reduce to [0..2] support
y = y[0:len(x)]
#Normalize (so that all values sum to 1/res)
y = y / (sum(y) * res)
return y
def fit_beta_by_pt(nsteps):
"""Fit a beta distribution by parallel tempering."""
num_dimensions = 1
# Create the dummy model
b = BetaFit(0.5, 0.5)
# Create the options
opts = MCMCOpts()
opts.model = b
opts.estimate_params = b.parameters
opts.initial_values = [10 ** 0.5]
opts.nsteps = nsteps
opts.anneal_length = 0
opts.T_init = 1
opts.use_hessian = False
opts.seed = 1
opts.norm_step_size = 0.5
opts.likelihood_fn = b.likelihood
opts.step_fn = step
# Create the MCMC object
num_temps = 8
pt = PT_MCMC(opts, num_temps, 10)
pt.estimate()
plt.ion()
for chain in pt.chains:
fig = plt.figure()
chain.prune(nsteps/10, 1)
(heights, points, lines) = plt.hist(chain.positions, bins=100,
normed=True)
plt.plot(points, beta.pdf(points, b.a, b.b), 'r')
plt.ylim((0,10))
plt.xlim((0, 1))
return pt
def fit_beta(nsteps):
"""Fit a beta distribution by MCMC."""
num_dimensions = 1
# Create the dummy model
b = BetaFit(0.5, 0.5)
# Create the options
opts = MCMCOpts()
opts.model = b
opts.estimate_params = b.parameters
opts.initial_values = [1.001]
opts.nsteps = nsteps
opts.anneal_length = nsteps/10
opts.T_init = 100
opts.use_hessian = False
opts.seed = 1
opts.norm_step_size = 0.01
opts.likelihood_fn = b.likelihood
opts.step_fn = step
# Create the MCMC object
mcmc = MCMC(opts)
mcmc.initialize()
mcmc.estimate()
mcmc.prune(nsteps/10, 1)
plt.ion()
for i in range(mcmc.num_estimate):
plt.figure()
(heights, points, lines) = plt.hist(mcmc.positions[:,i], bins=100,
normed=True)
plt.plot(points, beta.pdf(points, b.a, b.b), 'r')
return mcmc
def plot(a, b, trial, ctr):
x = np.linspace(0, 1, 200)
y = beta.pdf(x, a, b)
mean = float(a) / (a + b)
plt.plot(x, y)
plt.title("Distributions after %s trials, true rate = %.1f, mean = %.2f" % (trial, ctr, mean))
plt.show()
def plot_beta(name, a, b, ret=None, n=None):
print(a, b)
theta = linspace(0, 1, 300)
pdf = beta.pdf(theta, a, b)
ax = axes()
ax.plot(theta, pdf / max(pdf))
if n is not None:
ax.text(0.025, 0.9, 'TRADE IDEA %d' % n)
if ret is not None:
ax.text(0.025, 0.85, 'RETURN %s 0' % ('>' if ret else '<'))
ax.set_title('P(hit rate | ideas so far)')
ax.yaxis.set_ticks([])
ax.grid()
ax.legend()
ax.xaxis.set_label_text('Hit Rate')
ax.xaxis.set_ticks(linspace(0, 1, 11))
s, e = (beta.ppf(0.025, a, b), beta.ppf(0.975, a, b))
ax.fill([s, s, e, e, s], [0, 1, 1, 0, 0], color='0.9')
gcf().set_size_inches(10, 6)
savefig(name, bbox_inches='tight')
plt.close()
return
def setBetaDistribution(self): """ """ choosePrimObj = [.25,.75] choosePrimObj = beta.pdf(choosePrimObj,1.3+self.primCount,1) choosePrimObj /= choosePrimObj.sum() self.choosePrimObj = choosePrimObj
def bernoulli_posterior(data, prior):
n_1 = sum(data)
n_2 = len(data) - n_1
x = np.arange(0, 1.01, step=.01)
y = beta.pdf(x, prior[0] + n_1 - 1, prior[1] + n_2 - 1)
plt.plot(x, y)
plt.show()
def plot_beta(a, b, fill = False):
betap = partial(beta.pdf, a, b)
xs = np.linspace(0,1, num = 150)
#y = list(map(betap, x))
y = [beta.pdf(x, a, b) for x in xs]
fills = [ x <= 0.5 for x in xs]
#sio = cStringIO.StringIO()
sio = io.BytesIO()
#sio = io.StringIO()
fig = plt.figure()
ax1 = fig.add_subplot(111)
#ax1.set_ylim(bottom=0)
#ax1.set_ylim(bottom
if fill: ax1.fill_between(xs ,y,0,where=fills , color='0.8')
ax1.plot(xs, y)
fig.savefig(sio, format='png')
enc = base64.b64encode(sio.getvalue())
plt.close("all")
#hex = enc.strip()
hex = enc.decode('utf-8')
#hex = sio.getvalue().encode("base64").strip()
prob = beta.cdf(0.5, a, b)
return hex, prob
def prob_noisy(given, bayes, noise_type, noise):
"""Prob of saying given when rational bayesian would say bayes
Args: given: in [0,1], what respondent said
bayes: in [0, 1]
model_params: dict, incl keys with noise info
Returns: prob in [0, 1]
Different types of noise models: beta, binomial, noiseless.
"""
if noise_type == "noiseless":
tolerance = 1e-04
if jmutils.fuzzy_equals(given, bayes, tolerance):
return 1
else:
return 0
elif noise_type == "binomial":
num_trials = noise
num_successes = int(given * num_trials)
#return binom.pmf(num_successes, num_trials, bayes)
return my_binom_pmf(num_successes, num_trials, bayes)
elif noise_type == "beta":
print("Optimize the beta stuff almost certainly!!!")
alpha_beta = jmutils.beta_shape(bayes, noise)
return beta.pdf(given, *alpha_beta)
elif noise_type == "truncnorm":
scale = noise
#return truncnorm.pdf(given, -bayes / scale, (1 - bayes) / scale,
# loc=bayes, scale=scale)
return mytruncpdf_01bounds(given, bayes, noise)
else:
print("Error: meta noise_type not specified correctly")
sys.exit()
def BernBeta(priorBetaAB,Data):
'''
priorBetaAB: Tuple of beta(a,b) parameters
'''
# For notational convenience, rename components of priorBetaAB:
a = priorBetaAB[0]
b = priorBetaAB[1]
fig, ax = plt.subplots(3, 1,figsize=(16, 16))
Theta = np.arange(0.001,1, 0.001) # points for plotting
pTheta = beta.pdf(Theta, a, b) # prior for plotting
ax[0].fill_between(Theta, beta.pdf(Theta, a, b))
ax[0].set(ylabel = 'test',title = 'Prior(beta)')
ax[1].fill_between(Theta, beta.pdf(Theta, a, b))
ax[1].set(xlabel = 'test',title = 'Prior(beta)')
ax[2].fill_between(Theta, beta.pdf(Theta, a, b))
ax[2].set(xlabel = 'test',title = 'Prior(beta)')
def plot(bandits, trial):
x = np.linspace(0, 1, 200)
for b in bandits:
y = beta.pdf(x, b.a, b.b)
plt.plot(x, y, label="real p: %.4f" % b.p)
plt.title("Bandit distributions after %s trials" % trial)
plt.legend()
plt.show()
def wat(x, s_n, ar): f = 1 for i in xrange(len(ar)): if i is s_n: f = f*beta.pdf(x, ar[i][0] + 1, ar[i][1] - ar[i][0] + 1) #f(Sa|Dt) #Dt is information available before reward else: f = f*beta.cdf(x, ar[i][0] + 1, ar[i][1] - ar[i][0] + 1) #F(S < Sa|Dt) #D is information available before reward return f
def one_vote(N, threshold=0.5, ab=False, forecasts=False, normal=False, p=False, diagnostic=False):
import numpy as np
if sum(map(bool,[ab, forecasts, normal, p])) != 1:
raise ValueError("Please specify one and only one of the 'ab', 'forecasts', 'normal', or 'p' options.")
if ab:
a, b = ab
elif forecasts:
from scipy.stats import beta
a, b, _, _ = beta.fit(forecasts, floc=0, fscale=1)
elif normal:
from functions import fit_beta_to_normal
m, s = normal
a, b = fit_beta_to_normal(m,s)
else:
pass
if p:
from functions import mp_binom
victory_pr = mp_binom(np.ceil(N*threshold),N,p)
if (N*threshold).is_integer():
# tie probability in case N*threshold is whole:
tie_pr = mp_binom(N*(1-threshold)+1,N,p)
elif not (N*threshold).is_integer() and threshold != 0.5:
# tie probability in case N*threshold is not whole:
tie_pr = mp_binom(N*(1-threshold),N,p)
else:
tie_pr = 0
elif a and b:
from functions import beta_binomial
victory_pr = beta_binomial(np.ceil(N*threshold),N,a,b,multi_precission=True)
if (N*threshold).is_integer():
# tie probability in case N*threshold is whole:
tie_pr = beta_binomial(N*(1-threshold)+1,N,a,b,multi_precission=True)
elif not (N*threshold).is_integer() and threshold != 0.5:
# tie probability in case N*threshold is not whole:
tie_pr = beta_binomial(N*(1-threshold),N,a,b,multi_precission=True)
else:
tie_pr = 0
else:
pass
if diagnostic:
import matplotlib.pyplot as plt
x = np.linspace(0,1,1000)
try:
plt.style.use('http://chymera.eu/matplotlib/styles/chymeric-gnome.mplstyle')
except ValueError:
plt.style.use('ggplot')
plt.axvline(x=threshold, color="#fbb4b9", linewidth=1)
plt.legend(['percentage\n threshold'], loc='upper right')
plt.plot(x, beta.pdf(x,a,b))
plt.xlabel('Reference Candidate Vote Share')
plt.ylabel('PDF')
plt.show()
total_pr = victory_pr+tie_pr
return total_pr, victory_pr, tie_pr
def main():
""" Two beta distributions with the same mean but differing variance.
The distributions reflect kicking averages of a player who has been very
consistent through his career verses another who has had a range of better
and worse years. On the x-axis, we see the expected score ratio over the
season - the y-axis indicating the likelihood of that average
The prime graphs (lighter colouring) show the change in belief for the
players' expected season average after 4 kicks (trials) where only 1 was
successful. The more consistent player's season expectation changes very
little relative to the more varied player.
Adapted from this explanation:
http://varianceexplained.org/statistics/beta_distribution_and_baseball/
"""
a_1 = 61
b_1 = 20
a_2 = 610
b_2 = 200
this_season_scores = 1
this_season_misses = 3
print(mean(a_1, b_1))
print(mean(a_2, b_2))
x = np.linspace(0, 1, 1000)
y_1 = beta.pdf(x, a_1, b_1)
y_2 = beta.pdf(x, a_2, b_2)
y_1_prime = beta.pdf(x, a_1 + this_season_scores, b_1 + this_season_misses)
y_2_prime = beta.pdf(x, a_2 + this_season_scores, b_2 + this_season_misses)
plt.plot(x, y_1, 'r', lw=2, alpha=0.8, label='a = 61, b = 20')
plt.plot(x, y_1_prime, 'r', lw=2, alpha=0.2, label='a = 62, b = 23')
plt.plot(x, y_2, 'b', lw=2, alpha=0.8, label='a = 610, b = 200')
plt.plot(x, y_2_prime, 'b', lw=2, alpha=0.2, label='a = 611, b = 203')
plt.xlim(0.3, 1.0)
plt.xlabel('p(scoring)')
plt.ylabel('probability density')
plt.legend(loc='upper left')
plt.show()
def betadist(betaparams,B,pcF):
#defining beta distribution parameters
a=float(betaparams['a'].value)
b=float(betaparams['b'].value)
loc=float(betaparams['loc'].value)
scale=float(betaparams['scale'].value)
#creating fitted data
model_pcF=beta.pdf(B,a,b,loc=loc,scale=scale)
#returning residual
return (model_pcF-pcF)
def plotBayesian(aprior, bprior, apost, bpost):
x = np.linspace(0.2, 0.8, 1001)
prior = beta.pdf(x, aprior, bprior)
post = beta.pdf(x, apost, bpost)
plt.plot(x, prior, color='m', linewidth = 3)
plt.plot(x, post, color='g', linewidth = 3)
priormax = aprior/(aprior+bprior)
postmax = apost/(apost+bpost)
plt.plot([priormax, priormax], [0, 6], color='r', linewidth=3)
plt.plot([postmax, postmax], [0, 6], color='r', linewidth=3)
plt.title('Prior and Posterior Probability Distrubution for Florida', fontsize=36)
plt.xlabel('Probability that Obama wins', fontsize=28)
plt.ylabel('Probability Density', fontsize=28)
plt.tick_params(labelsize=20)
magenta_line = mpatches.Patch(color='magenta', label='Prior Distribution')
green_line = mpatches.Patch(color='green', label='Posterior Distribution')
red_line = mpatches.Patch(color='red', label='Most Likely Probability')
plt.legend(handles=[magenta_line, green_line, red_line], prop={'size': 24})
plt.show()
def pEmission(self,z,x):
"""
Returns a number proportional to the probability of x given z
"""
# TODO: each poll should be chosen according to a multinomial distribution rather than a dirichlet distribution
res = 1
for i in xrange(self.num_polls):
alpha = self.b[i]*z
res *= Beta.pdf(x[i,0], alpha[0], alpha[1])
return res
def main():
a = 81
b = 219
stats = beta.stats(a, b, moments='mvsk')
print('Mean: %.3f, Variance: %.3f, Skew: %.3f, Kurtosis: %.3f' % stats)
x = np.linspace(0, 1, 1000)
y = beta.pdf(x, a, b)
plt.plot(x, y, 'r-', lw=2, alpha=0.6, label='beta pdf')
plt.legend()
plt.show()
def plot_stats():
data = request.form
x = np.linspace(0.1,0.6,200)
y_a = beta.pdf(x, 1 + int(data['conversion_a']), 1 + int(data['allocation_a']) - int(data['conversion_a']))
y_b = beta.pdf(x, 1 + int(data['conversion_b']), 1 + int(data['allocation_b']) - int(data['conversion_b']))
plt.plot(x, y_a, label='A')
plt.plot(x, y_b, label='B')
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
image_data = base64.b64encode(buf.read())
response = Response(response=image_data, status=200)
buf.close()
plt.close()
return response
def _nll(param, data):
"""
Negative log likelihood function for beta distribution
"""
from scipy.stats import beta
a, b = param
pdf = beta.pdf(data, a, b)
lg = np.log(pdf)
mask = np.isfinite(lg)
nll = -lg[mask].sum()
return nll
def inferPosterior(self, state, action, prior='uniform'): """ Uses inference engine to compute posterior probability from the likelihood and prior (beta distribution). """ if prior == 'beta': # Beta Distribution self.prior = np.linspace(.01,1.0,101) self.prior = beta.pdf(self.prior,1.4,1.4) self.prior /= self.prior.sum() elif prior == 'shiftExponential': # Shifted Exponential self.prior = np.zeros(101) for i in range(50): self.prior[i + 50] = i * .02 self.prior[100] = 1.0 self.prior = expon.pdf(self.prior) self.prior[0:51] = 0 self.prior *= self.prior self.prior /= self.prior.sum() elif prior == 'shiftBeta': # Shifted Beta self.prior = np.linspace(.01,1.0,101) self.prior = beta.pdf(self.prior,1.2,1.2) self.prior /= self.prior.sum() self.prior[0:51] = 0 elif prior == 'uniform': # Uniform self.prior = np.zeros(len(self.sims)) self.prior = uniform.pdf(self.prior) self.prior /= self.prior.sum() self.posterior = self.likelihood * self.prior self.posterior /= self.posterior.sum()
def set_prior(self, theta_min, theta_max, num_theta, dist_name):
self.theta_val = np.linspace(theta_min, theta_max, num=num_theta)
self.num_theta = num_theta
if dist_name == 'uniform':
# the density is uniform
self.theta_density = np.ones(num_theta) / num_theta
elif dist_name == 'beta':
# centered beta
# rescale to move away from the boundary
self.theta_density = beta.pdf((self.theta_val - theta_min) / (theta_max - theta_min + 0.1), 2, 2)
# renormalize
self.theta_density = self.theta_density / sum(self.theta_density)
else:
raise Exception('Unknown prior distribution.')
def betaNLL(param,*args):
'''Negative log likelihood function for beta
<param>: list for parameters to be fitted.
<args>: 1-element array containing the sample data.
Return <nll>: negative log-likelihood to be minimized.
'''
a,b=param
data=args[0]
pdf=beta.pdf(data,a,b,loc=0,scale=1)
lg=np.log(pdf)
#-----Replace -inf with 0s------
lg=np.where(lg==-np.inf,0,lg)
nll=-1*np.sum(lg)
return nll
def get_pdf(self, x):
return beta.pdf(x, self.alpha, self.beta)
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
from scipy.stats import beta
fig, ax = plt.subplots(1, 1, figsize=(3.5, 4.5))
plt.tick_params(axis='both', which='major', labelsize=14)
a, b = .5, 1.5
x = np.linspace(beta.ppf(0.00, a, b), beta.ppf(1, a, b), 100)
# ax.plot(x, beta.pdf(x, a, b),'k.', lw=2, alpha=1, label='beta pdf')
a, b = 4, 12
x = np.linspace(beta.ppf(0., a, b), beta.ppf(1, a, b), 100)
ax.plot(x, beta.pdf(x, a, b),'k:', lw=2, alpha=1,
label='$(4, 12)$')
a, b = 1, 3
x = np.linspace(beta.ppf(0.05, a, b), beta.ppf(1, a, b), 100)
ax.plot(x, beta.pdf(x, a, b),'k--', lw=2, alpha=1, label='$(1, 3)$')
a, b = 2, 6
x = np.linspace(beta.ppf(0.00, a, b), beta.ppf(1, a, b), 1000)
ax.plot(x, beta.pdf(x, a, b),'k-', lw=2, alpha=1, label='$(2, 6)$')
a, b = 0.25, .75
x = np.linspace(beta.ppf(0.3, a, b), beta.ppf(.99, a, b), 200)
ax.plot(x, beta.pdf(x, a, b),'k-.', lw=2, alpha=1, label='$(0.25, 0.75)$')
def z_prob(z):
return ([
beta.pdf(xv, a1, b1) * beta.pdf(xv / z, a2, b2)
for xv in np.linspace(0.1, 1, 100)
])
mpl.rc("font", family="serif")
def likelihood(theta, n, k):
return binom(n, k) * theta**k * (1 - theta)**(n - k)
n = 11
k = 8
a = 2
b = 2
X = np.linspace(0, 1, num=1000)
t = likelihood(
X, n, k) * gamma(n + 2) / (gamma(k + 1) * gamma((n - k) + 1) * binom(n, k))
prior = beta.pdf(X, a, b)
posterior = beta.pdf(X, a + k, b + (n - k))
fig, ax = plt.subplots(figsize=(7, 7 / 1.4))
y_max = 4
turq = mpl.colors.to_rgb("turquoise")
mag = mpl.colors.to_rgb("magenta")
mix = [(turq[i] + mag[i]) / 2 for i in range(3)]
ax.plot(X, prior, color=turq, label="Prior", zorder=2)
ax.plot(X, t, color=mag, label="Likelihood (normalized)", zorder=2)
ax.plot(X, posterior, color=mix, label="Posterior", zorder=2)
theta_map = (a + k - 1) / (a + b + n - 2)
posterior_max = beta.pdf(theta_map, a + k, b + (n - k))
#And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show() #beta Continuous distributions¶ from scipy.stats import beta import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) #Calculate a few first moments: a, b = 2.31, 0.627 mean, var, skew, kurt = beta.stats(a, b, moments='mvsk') #Display the probability density function (pdf): x = np.linspace(beta.ppf(0.01, a, b), beta.ppf(0.99, a, b), 100) ax.plot(x, beta.pdf(x, a, b), 'r-', lw=5, alpha=0.6, label='beta pdf') #Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed. #Freeze the distribution and display the frozen pdf: rv = beta(a, b) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') #Check accuracy of cdf and ppf: vals = beta.ppf([0.001, 0.5, 0.999], a, b) np.allclose([0.001, 0.5, 0.999], beta.cdf(vals, a, b)) True #Generate random numbers: r = beta.rvs(a, b, size=1000) #And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()
if '-Nexp' in sys.argv:
p = sys.argv.index('-Nexp')
M = int(sys.argv[p+1])
if M > 0:
M_exp = M
else:
print('Enter positive Nexp arg.')
#the probability distribution from which to sample --> beta function with parameters a, b
a,b=7,3
x = np.arange (0.01, 1, 0.01)
y = scipybeta.pdf(x,a,b)
plt.figure()
plt.plot(x,y,label = r'$\alpha =$' + str(a) +', '+ r'$\beta =$' + str(b))
plt.xlabel('x',fontsize=16)
plt.ylabel(r'Beta($\alpha, \beta$)',fontsize=16)
plt.legend(fontsize=10)
plt.title('Non-Normalized, Continuous Prob Distribution',fontsize=15)
plt.show()
average_list = []
for i in range(0,M_exp):
vals=[]
for i in range(0,N_samples):
vals.append(npbeta(a,b))
vals = np.asarray(vals)
#Q1
#Two lists alpha1 and alpha2 contains 5 pairs of values with (a1,a2) pair as (alpha1[i],alpha2[i]) for i from 0 to 4
alpha1 = [2, 1, 3, 4, 7]
alpha2 = [3, 9, 5, 4, 5]
#Q2
#x_star list containing x* values for each pair
x_star = []
for i in range(5):
x_star.append((alpha1[i] - 1) / (alpha1[i] + alpha2[i] - 2))
#Q3
#f list contains f(x*) values for respective x* values
f = []
for i in range(5):
c = beta.pdf(x_star[i], alpha1[i], alpha2[i])
f.append(c)
# print(c)
print(x_star)
print(f)
#Q4 and Q5
#acceptance rejection method and histogram plots
bin = [
0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65,
0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1
]
for i in range(5):
c = f[i]
U1 = []
a = nm.random.random_sample()
f_a = beta.pdf(a, alpha1[i], alpha2[i])
'scale': 10,
'mixture': 0.8
}
# Proposed values are a Gaussian peturbation away from the previous values.
# This is controlled by the sigma of the gaussian, which is defined for each variable
proposal_sigma = {
'missing': 0.025,
'shape': 0.05,
'scale': 2,
'mixture': 0.025,
}
# PRIORS
priors = (lambda x: {
'missing': beta.pdf(x['missing'], a=3, b=15),
'mixture': beta.pdf(x['mixture'], a=1.1, b=1.1),
'shape': gma.pdf(x['shape'], a=10, scale=1 / 5),
'scale': gma.pdf(x['scale'], a=6, scale=50)
})
def test_mcmc():
folder = os.path.dirname(os.path.abspath(__file__))
file = "/mcmc_test_chain"
chain = mcmc.run_MCMC(data=am_data,
initial_parameters=initial_parameters,
proposal_sigma=proposal_sigma,
priors=priors,
thin=1,
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import beta, norm
T = 501
true_ctr = 0.5
a, b = 1, 1
plot_indices = (10, 20, 30, 50, 100, 200, 500)
data = np.empty(T)
for i in range(T):
x = 1 if np.random.random() < true_ctr else 0
data[i] = x
a += x
b += 1 - x
if i in plot_indices:
p = data[:i].mean()
n = i + 1
std = np.sqrt(p * (1 - p) / n)
x = np.linspace(0, 1, 200)
g = norm.pdf(x, loc=p, scale=std)
plt.plot(x, g, label="Gaussian Approximation")
posterior = beta.pdf(x, a=a, b=b)
plt.plot(x, posterior, label="Beta Posterior")
plt.legend()
plt.title(f"N = {n}")
plt.show()
def get_arrays_for_plotting(self):
to_plot = np.linspace(0, 1, 100)
y_plot = beta_dist.pdf(to_plot, self.alpha, self.beta)
return to_plot, y_plot
label="true pdf")
ax[0, col].plot(xgrid,
pdf_est * n_samples * (xgrid[1] - xgrid[0]),
'r',
label="estimated pdf")
if i == 0:
ax[0, i].legend()
ax[0, col].set_title(
("After %d iterations\n" +
"($\\mathrm{E}_q[\\tau]$=%.3f, $\\mathrm{E}_q[\\theta]$=%.3f)") %
(i + 1, tau_est, theta_est))
ax[0, col].set_xlabel("$x$")
# plot marginal distribution of tau
tau = np.linspace(0, 1.0, 1000)
q_tau = beta.pdf(tau, N2 + alpha0, N1 + alpha0)
ax[1, col].plot(tau, q_tau)
ax[1, col].set_xlabel("$\\tau$")
# plot marginal distribution of theta
theta = np.linspace(-4.0, 8.0, 1000)
q_theta = norm.pdf(theta, m2, np.sqrt(1 / beta2))
ax[2, col].plot(theta, q_theta)
ax[2, col].set_xlabel("$\\theta$")
col = col + 1
# finalize the plot
ax[1, 0].set_ylabel("$q(\\tau)$")
ax[2, 0].set_ylabel("$q(\\theta)$")
plt.tight_layout()
plt.show()
plt.legend(loc='best')
plt.show()
a_1 = np.linspace(0,10,100)
a_2 = np.linspace(0,10,100)
b_1 = np.linspace(0,10,100)
b_2 = np.linspace(0,10,100)
pi = np.linspace(0,1,10)
input_space = np.linspace(0,1,1000)
for i in range(5):
pi_rvs = randint.rvs(0,10)
a_1_rvs = randint.rvs(0,100)
a_2_rvs = randint.rvs(0,100)
b_1_rvs = randint.rvs(0,100)
b_2_rvs = randint.rvs(0,100)
bibeta_example_pdf = pi[pi_rvs]*beta.pdf(input_space,a_1[a_1_rvs],b_1[b_1_rvs]) + (1-pi[pi_rvs])*beta.pdf(input_space,a_2[a_2_rvs],b_2[b_2_rvs])
bibeta_example_cdf = pi[pi_rvs]*beta.cdf(input_space,a_1[a_1_rvs],b_1[b_1_rvs]) + (1-pi[pi_rvs])*beta.cdf(input_space,a_2[a_2_rvs],b_2[b_2_rvs])
ax = plt.subplot(111)
ax.plot(input_space, pi[pi_rvs]*beta.pdf(input_space,a_1[a_1_rvs],b_1[b_1_rvs]), label="1 comp pdf")
ax.plot(input_space, (1-pi[pi_rvs])*beta.pdf(input_space,a_2[a_2_rvs],b_2[b_2_rvs]), label="2 comp pdf")
ax.plot(input_space, pi[pi_rvs]*beta.pdf(input_space,a_1[a_1_rvs],b_1[b_1_rvs]) + (1-pi[pi_rvs])*beta.pdf(input_space,a_2[a_2_rvs],b_2[b_2_rvs]), label="Mix pdf")
plt.legend(loc='best')
plt.show()
ax = plt.subplot(111)
ax.plot(input_space, pi[pi_rvs]*beta.cdf(input_space,a_1[a_1_rvs],b_1[b_1_rvs]), label="1 comp cdf")
ax.plot(input_space, (1-pi[pi_rvs])*beta.cdf(input_space,a_2[a_2_rvs],b_2[b_2_rvs]), label="2 comp cdf")
ax.plot(input_space, pi[pi_rvs]*beta.cdf(input_space,a_1[a_1_rvs],b_1[b_1_rvs]) + (1-pi[pi_rvs])*beta.cdf(input_space,a_2[a_2_rvs],b_2[b_2_rvs]), label="Mix cdf")
plt.legend(loc='best')
plt.show()
bibeta_example_pdf = pi[pi_rvs]*beta.pdf(input_space,a_1[a_1_rvs],b_1[b_1_rvs]) + (1-pi[pi_rvs])*beta.pdf(input_space,a_2[a_2_rvs],b_2[b_2_rvs])
def p_z(z, include_gt):
return np.sum([
beta.pdf(xv, a1, b1) * beta.pdf(xv / z, a2, b2)
for xv in np.linspace(0.1, 1, 100)
]) + (gt_prob * beta.pdf(z, 60, 3) if include_gt else 0)
def get_gene_info(
*,
annotated_vcf,
variant_col,
af_col,
alt_col='Alt',
del_col,
output_dir,
genes_col,
maf_threshold=0.01,
beta_param,
weight_func='beta'
):
"""
Create temporary files with variant information for each gene, plus the weights calculated.
Parameters
----------
annotated_vcf : str
a file containing the variant, AF, ALT, Gene, and deleterious score.
variant_col : str
the name of the variant column.
af_col : str
the name of the Allele Frequency column.
alt_col : str
the name of the alternate allele column.
del_col : str
the name of functional annotation column.
output_dir : str
directory to save in temporary files.
genes_col : str
the name of genes column.
maf_threshold : float
between [0.0-1.0]. the minor allele frequency threshold, default is 0.01.
beta_param : tuple
the parameters of the beta function, if chosen for weighting.
weight_func : str
the weighting function, beta or log10.
Returns
-------
output directory with all the temporary files.
"""
skip = 0
if annotated_vcf.endswith('.gz'):
with gzip.open(annotated_vcf, 'r') as fin:
for line in fin:
if line.decode('utf-8').startswith('##'):
skip += 1
else:
with open(annotated_vcf, 'r') as file:
for line in file:
if line.startswith('##'):
skip += 1
df = pd.read_csv(annotated_vcf, usecols=[variant_col, alt_col, 'INFO'], skiprows=skip, sep=r'\s+', index_col=False)
info = df['INFO'].str.split(pat=';', expand=True)
missing_info = info[info.isnull().any(axis=1)].index
df.drop(missing_info, inplace=True)
df.reset_index(drop=True, inplace=True)
info.drop(missing_info, inplace=True)
info.reset_index(drop=True, inplace=True)
for col in info.columns:
val = info[col][0].split('=')
if len(val) == 1:
continue
info.rename(columns={col: val[0]}, inplace=True)
info[val[0]] = info[val[0]].str.replace(val[0] + "=", "")
df = pd.concat([df, info], axis=1)
df = df[df[af_col].values.astype(float) < maf_threshold]
df.replace('.', 0.0, inplace=True)
if weight_func == 'beta':
df[weight_func] = beta.pdf(df[af_col].values.astype(float), beta_param[0], beta_param[1])
elif weight_func == 'log10':
df[weight_func] = -np.log10(df[af_col].values.astype(float))
df[weight_func].replace([np.inf, -np.inf, np.nan], 0.0, inplace=True)
df['score'] = df[weight_func].values.astype(float) * df[del_col].values.astype(float)
genes = list(set(df[genes_col]))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
gene_file = output_dir + '.genes'
with open(gene_file, 'w') as f:
f.writelines("%s\n" % gene for gene in genes)
[df[df[genes_col] == gene][[variant_col, alt_col, 'score', genes_col]].to_csv(os.path.join(output_dir, (
str(gene) + '.w')), index=False, sep='\t') for gene in tqdm(genes, desc="writing w gene files")]
[df[df[genes_col] == gene][[variant_col, alt_col]].to_csv(os.path.join(output_dir, (str(gene) + '.v')),
index=False, sep='\t') for gene in
tqdm(genes, desc="writing v gene files")]
return output_dir
## into a single number. This is not completely trivial, as you
## need to combine the negative and positive Z into it, but I
## think you can all work it out.
P_dependent *= P_D_positive * P_D_negative
P_independent *= P_D
print(
"Now calculate a posterior distribution for the relevant Bernoulli parameter. Focus on just one value of y for simplicity"
)
# First plot the joint distribution
prior_alpha = 1
prior_beta = 1
xplot = np.linspace(0, 1, 200)
pdf_p = beta.pdf(xplot, prior_alpha + positive_alpha,
prior_beta + positive_beta)
pdf_n = beta.pdf(xplot, prior_alpha + negative_alpha,
prior_beta + negative_beta)
pdf_m = beta.pdf(xplot, prior_alpha + positive_alpha + negative_alpha,
prior_beta + positive_beta + negative_beta)
n_figures += 1
plt.figure(n_figures)
plt.clf()
plt.plot(xplot, pdf_p)
plt.plot(xplot, pdf_n)
plt.plot(xplot, pdf_m)
plt.legend(["z=1", "z=-1", "marginal"])
plt.title("y=" + str(y))
print("Probability of independence: ",
P_independent / (P_independent + P_dependent))
import numpy as np
from scipy.stats import beta
import matplotlib.pylab as plt
x = np.linspace(0, 1)
unif = beta.pdf(x, 1, 1)
cent = beta.pdf(x, 2.3, 2.3)
cent2 = beta.pdf(x, 12, 12)
skewed = beta.pdf(x, 3, 1)
plt.figure(figsize=(13, 4))
plt.subplot(1, 5, 1)
plt.plot(x, unif)
plt.ylim((0, 4))
plt.title('(A)')
plt.ylabel('rozdělení $p$')
plt.xlabel('p')
plt.subplot(1, 5, 2)
plt.plot(x, cent)
plt.title('(B1)')
plt.xlabel('p')
plt.ylim((0, 4))
plt.subplot(1, 5, 3)
plt.plot(x, cent2)
plt.title('(B2)')
plt.xlabel('p')
plt.ylim((0, 4))
plt.subplot(1, 5, 4)
plt.plot(x, skewed)
plt.ylim((0, 4))
plt.xlabel('p')
在程序中，使用a代表alpha，b代表beta
一个确定的概率密度函数，有其均值和标准差
通过beta分布的alpha和beta可以直接求出此分布的mu和sigma
通过其mu和sigma也可以求出其alpha和beta
'''
import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
sns.set_palette("deep", desat=.6)
sns.set_context(rc={"figure.figsize": (8, 4)})
x = np.linspace(0, 1, 100)
params = [(0.5, 0.5), (1.0, 1.0), (4.0, 3.0), (2.0, 5.0), (6.0, 6.0)]
for p in params:
y = beta.pdf(x, p[0], p[1])
a = p[0] #mid alpha
b = p[1] #mid beta
u = a / (a + b) #mid mu
s = ((a * b) / ((a + b)**2 * (a + b + 1)))**0.5 #mid sigma
plt.plot(
x,
y,
label="$\\alpha=%.4f$, $\\beta=%.4f$, $\\mu=%.4f$, $\\sigma=%.4f$"
% (a, b, u, s))
plt.xlabel("$\\theta$, Fairness")
plt.ylabel("Density")
plt.legend(title="Parameters")
plt.show()
def sigma_lnprior(sigma, alpha_value, beta_value):
return np.log(beta.pdf(abs(sigma), alpha_value, beta_value))
def f_beta(thetal, mu, nu):
return beta.pdf(t(thetal), mu, nu)*2./np.pi
def coin_flip_posterior(h, n, r):
return beta.pdf(h, n + 1, n - r + 1)
def pdf(self, grades):
assert self.a is not None and self.b is not None, 'Params have not been set. First run .fit()'
x = np.linspace(0, 1, 101)
return x, beta.pdf(x, self.a, self.b)
def plot_beta_distribution(a, b):
x = np.linspace(0, 1, 1000)
y = beta.pdf(x, a=a, b=b)
plt.plot(x, y, lw=3, alpha=0.7, label='a=%s, b=%s' % (a, b))
20, 20, 20, 20, 20, 20, 20, 19, 19, 19, 19, 18, 18, 17, 20, 20, 20,
20, 19, 19, 18, 18, 25, 24, 23, 20, 20, 20, 20, 20, 20, 10, 49, 19,
46, 27, 17, 49, 47, 20, 20, 13, 48, 50, 20, 20, 20, 20, 20, 20, 20,
48, 19, 19, 19, 22, 46, 49, 20, 20, 23, 19, 22, 20, 20, 20, 52, 46,
47, 24, 14
])
M = len(y)
# plot the separate and pooled models
plt.figure(figsize=(8,10))
x = np.linspace(0, 1, 250)
# separate
plt.subplot(2, 1, 1)
lines = plt.plot(x, beta.pdf(x[:,None], y[:-1] + 1, n[:-1] - y[:-1] + 1),
linewidth=1)
# highlight the last line
line1, = plt.plot(x, beta.pdf(x, y[-1] + 1, n[-1] - y[-1] + 1), 'r')
plt.legend((lines[0], line1),
(r'Posterior of $\theta_j$', r'Posterior of $\theta_{71}$'))
plt.yticks(())
plt.title('separate model')
# pooled
plt.subplot(2, 1, 2)
plt.plot(x, beta.pdf(x, y.sum() + 1, n.sum() - y.sum() + 1),
linewidth=2, label=(r'Posterior of common $\theta$'))
plt.legend()
plt.yticks(())
plt.xlabel(r'$\theta$', fontsize=20)
beta_params_a = [1] * latent_dim
beta_params_a[0] = alpha
beta_params_b = [1] * latent_dim
#############################################################
# BUILD DATA SET
if data_set == 1:
# X ~ MP = Unif <-- Observed, thinned.
# Y ~ P = Beta <-- Target, unthinned.
# Weights = 1/M = P/(MP) = Beta/Unif = Beta
latent = np.random.uniform(0, 1, size=(data_num, latent_dim))
latent_unthinned = np.random.beta(beta_params_a, beta_params_b,
(data_num, latent_dim))
weights = vert(beta.pdf(latent[:, 0], alpha, 1.))
weights_unthinned = vert(beta.pdf(latent_unthinned[:, 0], alpha, 1.))
fixed_transform = np.random.normal(0, 1, size=(latent_dim, data_dim))
data = np.dot(latent, fixed_transform)
data_unthinned = np.dot(latent_unthinned, fixed_transform)
elif data_set == 2:
# X ~ MP = Beta <-- Observed, thinned.
# Y ~ P = Unif <-- Target, unthinned.
# Weights = 1/M = P/(MP) = Unif/Beta = 1/Beta
latent = np.random.beta(beta_params, beta_params, (data_num, latent_dim))
latent_unthinned = np.random.uniform(0, 1, size=(data_num, latent_dim))
weights = vert(1. / beta.pdf(latent[:, 0], alpha, 1.))
weights_unthinned = vert(1. / beta.pdf(latent_unthinned[:, 0], alpha, 1.))
def beta_pdf(x): return beta_rv.pdf(x, a=alpha_stat, b=beta_stat) x_marginal_pdfs = [beta_pdf]*num_vars
def test_multi_action_distribution(self):
"""Tests the MultiActionDistribution (across all frameworks)."""
batch_size = 1000
input_space = Tuple([
Box(-10.0, 10.0, shape=(batch_size, 4)),
Box(-2.0, 2.0, shape=(
batch_size,
6,
)),
Dict({"a": Box(-1.0, 1.0, shape=(batch_size, 4))}),
])
std_space = Box(-0.05, 0.05, shape=(
batch_size,
3,
))
low, high = -1.0, 1.0
value_space = Tuple([
Box(0, 3, shape=(batch_size, ), dtype=np.int32),
Box(-2.0, 2.0, shape=(batch_size, 3), dtype=np.float32),
Dict({"a": Box(0.0, 1.0, shape=(batch_size, 2), dtype=np.float32)})
])
for fw, sess in framework_iterator(session=True):
if fw == "torch":
cls = TorchMultiActionDistribution
child_distr_cls = [
TorchCategorical, TorchDiagGaussian,
partial(TorchBeta, low=low, high=high)
]
else:
cls = MultiActionDistribution
child_distr_cls = [
Categorical,
DiagGaussian,
partial(Beta, low=low, high=high),
]
inputs = list(input_space.sample())
distr = cls(np.concatenate([inputs[0], inputs[1], inputs[2]["a"]],
axis=1),
model={},
action_space=value_space,
child_distributions=child_distr_cls,
input_lens=[4, 6, 4])
# Adjust inputs for the Beta distr just as Beta itself does.
inputs[2]["a"] = np.clip(inputs[2]["a"], np.log(SMALL_NUMBER),
-np.log(SMALL_NUMBER))
inputs[2]["a"] = np.log(np.exp(inputs[2]["a"]) + 1.0) + 1.0
# Sample deterministically.
expected_det = [
np.argmax(inputs[0], axis=-1),
inputs[1][:, :3], # [:3]=Mean values.
# Mean for a Beta distribution:
# 1 / [1 + (beta/alpha)] * range + low
(1.0 /
(1.0 + inputs[2]["a"][:, 2:] / inputs[2]["a"][:, 0:2])) *
(high - low) + low,
]
out = distr.deterministic_sample()
if sess:
out = sess.run(out)
check(out[0], expected_det[0])
check(out[1], expected_det[1])
check(out[2]["a"], expected_det[2])
# Stochastic sampling -> expect roughly the mean.
inputs = list(input_space.sample())
# Fix categorical inputs (not needed for distribution itself, but
# for our expectation calculations).
inputs[0] = softmax(inputs[0], -1)
# Fix std inputs (shouldn't be too large for this test).
inputs[1][:, 3:] = std_space.sample()
# Adjust inputs for the Beta distr just as Beta itself does.
inputs[2]["a"] = np.clip(inputs[2]["a"], np.log(SMALL_NUMBER),
-np.log(SMALL_NUMBER))
inputs[2]["a"] = np.log(np.exp(inputs[2]["a"]) + 1.0) + 1.0
distr = cls(np.concatenate([inputs[0], inputs[1], inputs[2]["a"]],
axis=1),
model={},
action_space=value_space,
child_distributions=child_distr_cls,
input_lens=[4, 6, 4])
expected_mean = [
np.mean(np.sum(inputs[0] * np.array([0, 1, 2, 3]), -1)),
inputs[1][:, :3], # [:3]=Mean values.
# Mean for a Beta distribution:
# 1 / [1 + (beta/alpha)] * range + low
(1.0 / (1.0 + inputs[2]["a"][:, 2:] / inputs[2]["a"][:, :2])) *
(high - low) + low,
]
out = distr.sample()
if sess:
out = sess.run(out)
out = list(out)
if fw == "torch":
out[0] = out[0].numpy()
out[1] = out[1].numpy()
out[2]["a"] = out[2]["a"].numpy()
check(np.mean(out[0]), expected_mean[0], decimals=1)
check(np.mean(out[1], 0), np.mean(expected_mean[1], 0), decimals=1)
check(np.mean(out[2]["a"], 0),
np.mean(expected_mean[2], 0),
decimals=1)
# Test log-likelihood outputs.
# Make sure beta-values are within 0.0 and 1.0 for the numpy
# calculation (which doesn't have scaling).
inputs = list(input_space.sample())
# Adjust inputs for the Beta distr just as Beta itself does.
inputs[2]["a"] = np.clip(inputs[2]["a"], np.log(SMALL_NUMBER),
-np.log(SMALL_NUMBER))
inputs[2]["a"] = np.log(np.exp(inputs[2]["a"]) + 1.0) + 1.0
distr = cls(np.concatenate([inputs[0], inputs[1], inputs[2]["a"]],
axis=1),
model={},
action_space=value_space,
child_distributions=child_distr_cls,
input_lens=[4, 6, 4])
inputs[0] = softmax(inputs[0], -1)
values = list(value_space.sample())
log_prob_beta = np.log(
beta.pdf(values[2]["a"], inputs[2]["a"][:, :2],
inputs[2]["a"][:, 2:]))
# Now do the up-scaling for [2] (beta values) to be between
# low/high.
values[2]["a"] = values[2]["a"] * (high - low) + low
inputs[1][:, 3:] = np.exp(inputs[1][:, 3:])
expected_log_llh = np.sum(
np.concatenate([
np.expand_dims(
np.log(
[i[values[0][j]]
for j, i in enumerate(inputs[0])]), -1),
np.log(
norm.pdf(values[1], inputs[1][:, :3],
inputs[1][:, 3:])), log_prob_beta
], -1), -1)
values[0] = np.expand_dims(values[0], -1)
if fw == "torch":
values = tree.map_structure(lambda s: torch.Tensor(s), values)
# Test all flattened input.
concat = np.concatenate(tree.flatten(values),
-1).astype(np.float32)
out = distr.logp(concat)
if sess:
out = sess.run(out)
check(out, expected_log_llh, atol=15)
# Test structured input.
out = distr.logp(values)
if sess:
out = sess.run(out)
check(out, expected_log_llh, atol=15)
# Test flattened input.
out = distr.logp(tree.flatten(values))
if sess:
out = sess.run(out)
check(out, expected_log_llh, atol=15)
from scipy.stats import beta
import numpy as np
import matplotlib.pyplot as plt
import os
import pyprobml_utils as pml
x = np.linspace(0, 1, 100)
aa = [0.1, 1., 2., 8.]
bb = [0.1, 1., 3., 4.]
props = ['b-', 'r:', 'b-.', 'g--']
for a, b, p in zip(aa, bb, props):
y = beta.pdf(x, a, b)
plt.plot(y, p, lw=3, label='a=%.1f,b=%.1f' % (a, b))
plt.legend(loc='upper left')
pml.savefig('betaPlotDemo.png')
plt.show()
import scipy
from scipy.stats import beta
alphas = [2, 2, 1, 1]
betas = [2, 2, 1, 1]
Ns = [4, 40, 4, 40]
ks = [1, 10, 1, 10]
plots = ['betaPostInfSmallSample', 'betaPostInfLargeSample',
'betaPostUninfSmallSample', 'betaPostUninfLargeSample']
x = np.linspace(0.001, 0.999, 50)
for i in range(len(plots)):
alpha_prior = alphas[i]
beta_prior = betas[i]
N = Ns[i]
k = ks[i]
alpha_post = alpha_prior + N - k
beta_post = beta_prior + k
alpha_lik = N - k + 1
beta_lik = k + 1
pl.plot(x, beta.pdf(x, alpha_prior, beta_prior), 'r-',
label='prior Be(%2.1f, %2.1f)' % (alpha_prior, beta_prior))
pl.plot(x, beta.pdf(x, alpha_lik, beta_lik), 'k:',
label='lik Be(%2.1f, %2.1f)' % (alpha_lik, beta_lik))
pl.plot(x, beta.pdf(x, alpha_post, beta_post), 'b-',
label='post Be(%2.1f, %2.1f)' % (alpha_post, beta_post))
pl.legend(loc='upper left')
pl.savefig(plots[i] + '.png')
pl.show()
from scipy.stats import beta print(beta.pdf(2, 3, 3))
def dpbmm(data, num_iter, param=None):
# Beta value Dirichlet process mixture model, with no gap algorithm
s_data = np.shape(data)
G = copy.copy(s_data[0]) ## number of genes
C = copy.copy(s_data[1]) ## number of samples
count_sum = 100
# parameters for G0, Gamma(a, b)
a = 6
b = 5
if param is None:
param = params(mu_a=3.2,
mu_b=2.2,
sigma2_a=5,
sigma2_b=2,
k=C,
m_s=np.ones(C),
s=np.arange(C),
tau=5)
param.param_val(C, G)
for i in range(num_iter):
new_param = copy.deepcopy(param)
## Gibbs sampling
## step 1: resample s
s = copy.copy(new_param.s)
k = copy.copy(new_param.k)
m_s = copy.copy(new_param.m_s)
alpha_val = copy.copy(new_param.alpha_val)
beta_val = copy.copy(new_param.beta_val)
tau = copy.copy(new_param.tau)
mu_a = copy.copy(new_param.mu_a)
mu_b = copy.copy(new_param.mu_b)
sigma2a = copy.copy(new_param.sigma2_a)
sigma2b = copy.copy(new_param.sigma2_b)
for j in range(C):
s, k = rearrange_s(s)
m_s, k = calculate_m_s(s)
if m_s[s[j]] == 1:
u = np.random.rand()
if u < (k - 1) / k:
continue
ind = np.where(s == (k - 1))[0]
tmp = copy.copy(s[j])
s[ind] = copy.copy(tmp)
s[j] = k - 1
tmp_alpha_val = copy.copy(alpha_val[:, tmp])
alpha_val[:, tmp] = copy.copy(alpha_val[:, k - 1])
alpha_val[:, k - 1] = copy.copy(tmp_alpha_val)
tmp_beta_val = copy.copy(beta_val[:, tmp])
beta_val[:, tmp] = copy.copy(beta_val[:, k - 1])
beta_val[:, k - 1] = copy.copy(tmp_beta_val)
m_s, k = calculate_m_s(s, c=j)
p_x = np.zeros(k)
for l in range(k):
L_alpha_val, L_beta_val = exp_trans(
alpha_val[:, l], beta_val[:, l])
p_x[l] = np.prod(
beta.pdf(data[:, j], L_alpha_val, L_beta_val))
w = m_s / (tau + j) * p_x
if k < C:
L_alpha_val, L_beta_val = exp_trans(
alpha_val[:, k], beta_val[:, k])
# w[k+1] = tau/(tau+j-1) * np.prod(beta(data[:, j], L_alpha_val, L_beta_val))
temp1 = np.prod(
beta.pdf(data[:, j], L_alpha_val, L_beta_val))
p_x = np.append(p_x, temp1)
w = np.append(w, tau / (tau + j) * temp1)
population = np.arange(k + 1)
else:
population = np.arange(k)
w = w / np.sum(w)
s[j] = np.random.choice(population, size=1, p=w)
else:
# del m_s
# del p_x
# del w
m_s, k = calculate_m_s(s, c=j)
p_x = np.zeros(k)
for l in range(k):
L_alpha_val, L_beta_val = exp_trans(
alpha_val[:, l], beta_val[:, l])
p_x[l] = np.prod(
beta.pdf(data[:, j], L_alpha_val, L_beta_val))
w = m_s / (tau + j) * p_x
if k < C:
L_alpha_val, L_beta_val = exp_trans(
alpha_val[:, k], beta_val[:, k])
p_x = np.append(
p_x,
np.prod(beta.pdf(data[:, j], L_alpha_val, L_beta_val)))
w = np.append(w, p_x[k] * tau / (tau + j))
population = np.arange(k + 1)
else:
population = np.arange(k)
w = w / np.sum(w)
s[j] = np.random.choice(population, size=1, p=w)
s, k = rearrange_s(s)
m_s, k = calculate_m_s(s, c=0)
# new_alpha_val = np.zeros([G, k])
# new_beta_val = np.zeros([G, k])
for g in range(G):
for j in np.arange(k, C):
alpha_val[g, j] = np.random.normal(loc=mu_a,
scale=sigma2a,
size=1)
beta_val[g, j] = np.random.normal(loc=mu_b,
scale=sigma2b,
size=1)
V_a = sigma2a * np.ones([G, G])
V_b = sigma2b * np.ones([G, G])
for j in range(k):
new_alpha_val = np.random.multivariate_normal(
mean=alpha_val[:, j], cov=V_a)
new_beta_val = np.random.multivariate_normal(mean=beta_val[:,
j],
cov=V_b)
count = 0
accept = 0
c_1 = 0
ind = np.where(s == j)
while accept == 0:
for count_ind in range(count_sum):
p_xi = np.zeros(len(ind))
p_xi_t = np.zeros(len(ind))
new_alpha_val[g] = np.random.normal(loc=alpha_val[g,
j],
scale=sigma2a,
size=1)
# new_beta_val[g, i] = np.random.normal(loc=beta_val[g, i], scale=sigma2b, size=1)
# ind = np.where(s == j)
L_alpha_val, L_beta_val = exp_trans(
new_alpha_val, beta_val[:, j])
L_alpha_val_t, L_beta_val_t = exp_trans(
alpha_val[:, j], beta_val[:, j])
for m in range(len(ind)):
p_xi[m] = np.sum(
np.log(
beta.pdf(data[:, ind[m]], L_alpha_val,
L_beta_val)))
p_xi_t[m] = np.sum(
np.log(
beta.pdf(data[:, ind[m]], L_alpha_val_t,
L_beta_val_t)))
sum_p_xi = np.sum(p_xi)
sum_p_xi_t = np.sum(p_xi_t)
fx = np.dot(
norm.pdf(alpha_val[:, j], np.zeros(G), sigma2a),
norm.pdf(beta_val[:, j], np.zeros(G), sigma2b))
fx_t = np.dot(
norm.pdf(new_alpha_val, np.zeros(G), sigma2a),
norm.pdf(beta_val[:, j], np.zeros(G), sigma2b))
# Metropolis Hastings sampling
if sum_p_xi == sum_p_xi_t and sum_p_xi == -np.inf:
if fx == fx_t and fx == 0:
tmp = 0
else:
tmp = np.exp(np.log(fx) - np.log(fx_t))
else:
if fx == fx_t and fx == 0:
tmp = np.exp(sum_p_xi - sum_p_xi_t)
else:
if fx == 0 and sum_p_xi_t == -np.inf:
tmp = np.exp(sum_p_xi - np.log(fx_t))
elif fx_t == 0 and sum_p_xi == -np.inf:
tmp = np.exp(fx - sum_p_xi_t)
else:
tmp = np.exp(
np.log(fx) + sum_p_xi - np.log(fx_t) -
sum_p_xi_t)
u = np.random.rand()
# count_ind += 1
if u < tmp:
alpha_val[g, j] = new_alpha_val[g]
count += 1
c_1 += 1
if count >= 3 or c_1 > 4:
accept = 1
else:
sigma2a *= 0.98
if sigma2a < 0.01:
sigma2a = 0.01
if count > 20:
sigma2a /= 0.98
accept = 0
count = 0
c_2 = 0
while accept == 0:
for count_ind in range(count_sum):
new_beta_val[g] = np.random.normal(loc=beta_val[g, j],
scale=sigma2b,
size=1)
L_alpha_val, L_beta_val = exp_trans(
alpha_val[:, j], new_beta_val)
L_alpha_val_t, L_beta_val_t = exp_trans(
alpha_val[:, j], beta_val[:, j])
p_xi = np.zeros(len(ind))
p_xi_t = np.zeros(len(ind))
for m in range(len(ind)):
p_xi[m] = np.sum(
np.log(
beta.pdf(data[:, ind[m]], L_alpha_val,
L_beta_val)))
p_xi_t = np.sum(
np.log(
beta.pdf(data[:, ind[m]], L_alpha_val_t,
L_beta_val_t)))
sum_p_xi = np.sum(p_xi)
sum_p_xi_t = np.sum(p_xi_t)
fx = np.dot(
norm.pdf(alpha_val[:, j], np.zeros(G), sigma2a),
norm.pdf(beta_val[:, j], np.zeros(G), sigma2b))
fx_t = np.dot(
norm.pdf(new_alpha_val, np.zeros(G), sigma2a),
norm.pdf(new_beta_val, np.zeros(G), sigma2b))
# Metropolis Hastings sampling
if sum_p_xi == sum_p_xi_t and sum_p_xi == -np.inf:
if fx == fx_t and fx == 0:
tmp = 0
else:
tmp = np.exp(np.log(fx) - np.log(fx_t))
else:
if fx == fx_t and fx == 0:
tmp = np.exp(sum_p_xi - sum_p_xi_t)
else:
if fx == 0 and sum_p_xi_t == -np.inf:
tmp = np.exp(sum_p_xi - np.log(fx_t))
elif fx_t == 0 and sum_p_xi == -np.inf:
tmp = np.exp(fx - sum_p_xi_t)
else:
tmp = np.exp(
np.log(fx) + sum_p_xi - np.log(fx_t) -
sum_p_xi_t)
u = np.random.rand()
if u < tmp:
beta_val[g, j] = new_beta_val[g]
count += 1
c_2 += 1
if count >= 3 or c_2 > 4:
accept = 1
else:
sigma2b *= 0.98
if sigma2b < 0.01:
sigma2b = 0.01
if count > 20:
sigma2b /= sigma2b
# step 3: resampling mixture weights pi
if k == 1:
continue
m_s, k = calculate_m_s(s)
pi = dirichletrnd(m_s + tau / k)
# step 4: resampling concentration parameter tau
r = np.random.beta(a=tau + 1, b=C, size=1)
eta_r = 1 / (C * (b - np.log(r)) / (a + k - 1) + 1)
tmp = np.random.rand()
if tmp < eta_r:
tau_new = np.random.gamma(shape=a + k, scale=b - np.log(r), size=1)
else:
tau_new = np.random.gamma(shape=a + k - 1,
scale=b - np.log(r),
size=1)
tau = tau_new
# step 5: update parameters for the usage in the new iteration
new_param.s = s
new_param.k = k
new_param.m_s = m_s
new_param.alpha_val = alpha_val
new_param.beta_val = beta_val
new_param.tau = tau
new_param.pi = pi
new_param.sigma2_a = sigma2a
new_param.sigma2_b = sigma2b
param = copy.deepcopy(new_param)
file.write(str(i) + " iterations done")
print(param.s)
return param