def fit_beta_mixture(Y, gamma, n_itr=100):
Y = Y / gamma
Y = Y[(Y > 0) & (Y < 1)]
#plt.figure(figsize=[12,5])
#sns.distplot(Y)
#plt.show()
theta = np.array([1, 10, 2, 5, 0.5])
w = np.zeros([Y.shape[0], 2], dtype=float)
if_converge = False
for i in range(n_itr):
theta_old = theta
# E step
w[:, 0] = beta.pdf(Y, theta[0], theta[1]) * theta[4]
w[:, 1] = beta.pdf(Y, theta[2], theta[3]) * (1 - theta[4])
w = (w.T / w.sum(axis=1)).T
# M step
rand_idx = np.random.binomial(1, w[:, 0], size=Y.shape[0])
theta[4] = w[:, 0].mean()
print(np.sum(rand_idx == 1))
theta[0], theta[1], _, _ = beta.fit(Y[rand_idx == 1], loc=0, scale=1)
theta[2], theta[3], _, _ = beta.fit(Y[rand_idx == 0], loc=0, scale=1)
if np.linalg.norm(theta - theta_old) < 1e-8:
if_converge = True
break
return theta, if_converge
def get_outliers(data, filter, plotting):
if plotting:
for x, r in [("x1", (0, 1)), ("x2", (0, 30)), ("x3", (0, 1))]:
plt.violinplot(data[x], vert=False)
plt.xlim(r)
plt.savefig("plots/violin/%s.png" % x)
plt.clf()
if filter:
data_fl = data[data["class"] == 0]
else:
data_fl = data
pdf = pd.DataFrame({})
a, b, loc, scale = beta.fit(data_fl["x1"])
pdf["x1"] = beta.logpdf(data["x1"], a, b, loc=loc, scale=scale)
a, loc, scale = gamma.fit(data_fl["x2"])
pdf["x2"] = gamma.logpdf(data["x2"], a, loc=loc, scale=scale)
a, b, loc, scale = beta.fit(data_fl["x3"])
pdf["x3"] = beta.logpdf(data["x3"], a, b, loc=loc, scale=scale)
pdfs = pdf["x1"] + pdf["x2"] + pdf["x3"]
if plotting:
sns.boxplot(y=pdfs, x="class", data=data)
plt.savefig("plots/boxplot.png")
plt.clf()
if plotting:
plt.plot(np.sort(pdfs))
splits = [40, 45, 50, 60]
for split in splits:
split = np.sort(pdfs)[60]
plt.plot((0, 1000), (split, split), 'k-', lw=0.5)
split = np.sort(pdfs)[50]
plt.plot((0, 1000), (split, split), 'k.', lw=0.5)
split = np.sort(pdfs)[45]
plt.plot((0, 1000), (split, split), 'k--', lw=0.5)
split = np.sort(pdfs)[40]
plt.plot((0, 1000), (split, split), 'k--', lw=0.5)
plt.savefig("plots/thresholds.png")
plt.clf()
outliers = np.argsort(pdfs)
final = []
for outlier in outliers:
if data["class"][outlier] == -1:
final.append(outlier)
return np.array(final[:100])
def organize_data():
df = pd.read_csv('2016stats.csv')
df = df[df['3PA'] > 20]
df['3P%'] = df['3P%'] / 100
a = beta.fit(list(df['3P%']), floc=0, fscale=1)[0]
b = beta.fit(list(df['3P%']), floc=0, fscale=1)[1]
df['3PEstimate'] = (df['3PM'] + a) / (df['3PA'] + a + b)
df['a'] = df['3PM'] + a
df['b'] = df['3PA'] - df['3PM'] + b
print('alpha: ' + str(a))
print('beta: ' + str(b))
return (df, a, b)
def phase1(state):
"""
if state['pulls-left'] % 100 == 0:
print(state['pulls-left'])
"""
if state['pulls-left'] == 9000:
mikeys_ducks_info['alph-beta-scal'] = []
for i in range(100):
alph, beta, _, scal = beta_mod.fit(mikeys_ducks_info['payoffs'][i])
mikeys_ducks_info['alph-beta-scal'].append(
((scal * alph) / (alph + beta), alph, beta, scal, i))
elif state['pulls-left'] == 10000:
mikeys_ducks_info['costs'] = [0 for i in range(100)]
mikeys_ducks_info['metadata'] = ['00000000' for i in range(100)]
mikeys_ducks_info['payoffs'] = [[] for i in range(100)]
if check_key(state, 'last-cost'):
last_pull = mikeys_ducks_info['last-pull']
mikeys_ducks_info['utility'] += (state['last-payoff'] -
state['last-cost'])
mikeys_ducks_info['costs'][last_pull] = state['last-cost']
mikeys_ducks_info['metadata'][last_pull] = state['last-metadata']
mikeys_ducks_info['payoffs'][last_pull].append(state['last-payoff'])
if len(mikeys_ducks_info['payoffs'][last_pull]) >= 1000:
mikeys_ducks_info['machines-done'].add(last_pull)
if check_key(mikeys_ducks_info,
'alph-beta-scal') and state['pulls-left'] % 10 == 0:
alph, beta, _, scal = beta_mod.fit(
mikeys_ducks_info['payoffs'][last_pull])
mikeys_ducks_info['alph-beta-scal'][last_pull] = (scal * alph /
(alph + beta),
alph, beta, scal,
last_pull)
move = {}
move['team-code'] = state['team-code']
move['game'] = 'phase_1'
if state['pulls-left'] > 9000:
move['pull'] = int((10000 - state['pulls-left']) / 10)
else:
best_prof, best_ind = get_best_profit_index()
move['pull'] = best_ind
mikeys_ducks_info['last-pull'] = move['pull']
if state['pulls-left'] == 1:
mikeys_ducks_info['machines-done'].add(move['pull'])
mikeys_ducks_info['auctions'] = sorted(
list(mikeys_ducks_info['machines-done']),
key=lambda x: -1 * (mikeys_ducks_info['alph-beta-scal'][x][0] -
mikeys_ducks_info['costs'][x]))
return move
def organize_data(season):
df = pd.read_csv('Data/Seasons_Stats.csv')
df = df[df['Year'] == season]
df = remove_duplicate_players(df)
df = df[['Player', '3P', '3PA', '3P%', 'Tm', 'Year']]
df = df[df['3PA'] > 20]
a = beta.fit(list(df['3P%']), floc=0, fscale=1)[0]
b = beta.fit(list(df['3P%']), floc=0, fscale=1)[1]
df['3PEstimate'] = (df['3P'] + a) / (df['3PA'] + a + b)
df['a'] = df['3P'] + a
df['b'] = df['3PA'] - df['3P'] + b
print('alpha: ' + str(a))
print('beta: ' + str(b))
return (df, a, b)
def test_draw_samples(self, dtype, a_shape, a_is_samples, b_shape,
b_is_samples, rv_shape, num_samples):
# Note: Tests above have been commented as they are very slow to run.
# Note: Moved random number generation to here as the seed wasn't set if used above
a = np.random.uniform(0.5, 2, size=a_shape)
b = np.random.uniform(0.5, 2, size=b_shape)
n_dim = 1 + len(rv_shape)
a_np = numpy_array_reshape(a, a_is_samples, n_dim)
b_np = numpy_array_reshape(b, b_is_samples, n_dim)
rv_samples_np = np.random.beta(a_np,
b_np,
size=(num_samples, ) + rv_shape)
var = Beta.define_variable(shape=rv_shape, dtype=dtype,
rand_gen=None).factor
a_mx = mx.nd.array(a, dtype=dtype)
if not a_is_samples:
a_mx = add_sample_dimension(mx.nd, a_mx)
b_mx = mx.nd.array(b, dtype=dtype)
if not b_is_samples:
b_mx = add_sample_dimension(mx.nd, b_mx)
variables = {var.a.uuid: a_mx, var.b.uuid: b_mx}
rv_samples_rt = var.draw_samples(F=mx.nd,
variables=variables,
num_samples=num_samples)
assert np.issubdtype(rv_samples_rt.dtype, dtype)
assert is_sampled_array(mx.nd, rv_samples_rt)
assert get_num_samples(mx.nd, rv_samples_rt) == num_samples
rtol, atol = 1e-1, 1e-1
from itertools import product
fits_np = [
beta.fit(rv_samples_np[:, i, j])[0:2]
for i, j in (product(*map(range, rv_shape)))
]
fits_rt = [
beta.fit(rv_samples_rt.asnumpy()[:, i, j])[0:2]
for i, j in (product(*map(range, rv_shape)))
]
assert np.allclose(fits_np, fits_rt, rtol=rtol, atol=atol)
def make_first_set_of_plots():
N = 1000
x = zeros(shape=(N,), dtype=float)
t = None
tmax = 10
axis([0,tmax,0,1])
for i in range(N):
t, y = random_walk(0.25, tmax, 0.01, t)
x[i] = y[-1]
if (i < 3):
plot(t, (y+1)/2.0)
xlabel("time")
ylabel("CTR")
savefig("random_walk.png")
clf()
subplot(211)
hist((x+1)/2, bins=50)
ylabel("Monte carlo results")
subplot(212)
best_fit = beta.fit((x+1)/2, floc=0, fscale=1)
print best_fit
ctr = arange(0,1,0.001)
plot(ctr, beta(1,4).pdf(ctr), label="Invariant distribution, beta(1,4)")
plot(ctr, beta(best_fit[0],best_fit[1]).pdf(ctr), label="Best fit, beta("+str(best_fit[0]) + "," + str(best_fit[1]) + ")")
xlabel("CTR at t="+str(tmax))
ylabel("pdf")
legend()
savefig("long_term_random_walk_result.png")
def get_surprisal_continuous(infile):
"""
Generates a continuous probability distribution
for the surprisal metrics.
:param infile: str; path to file containing sequences
of discreet values.
:return:
"""
with open(infile, "r", encoding="utf8") as F:
sims = np.array([
j
for i in tqdm(F.readlines(), unit_scale=True, desc=os.path.basename(infile))
for j in np.fromstring(i.replace("nan", "").strip(), sep=" ", dtype=np.float32)
if i.strip()
])
sims = np.clip(sims, 0, 1)
print(sims.shape)
print("Fitting beta distribution to data.")
sims = np.random.choice(sims, size=20_000_000, replace=False)
B = beta.fit(sims)
print(B)
with open(f"../out/{os.path.basename(infile)}", "w", encoding="utf8") as F:
F.write(f"beta distribution\n{B}")
return 0
def _fit_beta(self, X):
"""Fit the beta parameters to the data.
"""
self.loc = np.min(X)
self.scale = np.max(X) - self.loc
self.a, self.b, _, _ = beta.fit(X, loc=self.loc, scale=self.scale)
self.model = self._get_model()
def make_first_set_of_plots():
N = 1000
x = zeros(shape=(N, ), dtype=float)
t = None
tmax = 10
axis([0, tmax, 0, 1])
for i in range(N):
t, y = random_walk(0.25, tmax, 0.01, t)
x[i] = y[-1]
if (i < 3):
plot(t, (y + 1) / 2.0)
xlabel("time")
ylabel("CTR")
savefig("random_walk.png")
clf()
subplot(211)
hist((x + 1) / 2, bins=50)
ylabel("Monte carlo results")
subplot(212)
best_fit = beta.fit((x + 1) / 2, floc=0, fscale=1)
print best_fit
ctr = arange(0, 1, 0.001)
plot(ctr, beta(1, 4).pdf(ctr), label="Invariant distribution, beta(1,4)")
plot(ctr,
beta(best_fit[0], best_fit[1]).pdf(ctr),
label="Best fit, beta(" + str(best_fit[0]) + "," + str(best_fit[1]) +
")")
xlabel("CTR at t=" + str(tmax))
ylabel("pdf")
legend()
savefig("long_term_random_walk_result.png")
def vcf_graph(rows, pop, binsize, title, filename):
expx = rows["ac_%s" % pop].divide(rows["an_%s" % pop])
expx = expx[((expx > 0) & (expx < 1))]
alphax, betax, _, _ = beta.fit(expx, floc=0, fscale=1)
x = np.arange(0, 1, binsize)
binx = [(x[i + 1] + x[i]) / 2 for i in range(len(x) - 1)]
y = [
btdtr(alphax, betax, x[i + 1]) - btdtr(alphax, betax, x[i])
for i in range(len(x) - 1)
]
fig = go.Figure()
fig.add_trace(
go.Histogram(x=expx,
histnorm='probability',
name="Experimental",
autobinx=False,
xbins=dict(start=0, end=1, size=binsize),
opacity=.9))
fig.add_trace(go.Bar(x=binx, y=y, name="Theory", opacity=.9))
fig.update_layout(autosize=False,
width=800,
height=600,
yaxis=go.layout.YAxis(title_text="P(x)", range=[0, 1]),
xaxis=go.layout.XAxis(title_text="x", range=[0, 1]),
title_text=title,
legend_orientation="h")
#fig.write_image(filename)
ksexp = kstest(expx, 'beta', args=(alphax, betax))
ksneut = kstest('beta', False, args=(alphax, betax), N=expx.size)
return (alphax, betax, ksexp.statistic, ksexp.pvalue, ksneut.statistic,
ksneut.pvalue, expx.size)
def ebb_fit_prior(x, n, method='mm', start=(0.5, 0.5)):
p = x / n
if (method == 'mm'):
mu, sig = np.mean(p), np.var(p)
a = ((1 - mu) / sig - 1 / mu) * mu**2
b = a * (1 / mu - 1)
fitted_prior = Beta(a, b)
pass
elif (method == 'mle'):
# starting value
# if (np.isnan(start)):
# mm_est = ebb_fit_prior(x, n, 'mm')
# start = (mm_est.alpha, mm_est.beta)
# #print(start)
# likelihood function: f(a, b)
def likelihood(pars):
return (-np.sum(beta_dist.pdf(p, pars[0], pars[1])))
# optimization function: over a series of params, optimise likelihood
# outp = minimize(likelihood, x0 = start, method = 'BFGS')
# fitted_prior = Beta(outp.x[0], outp.x[1])
a, b, *ls = beta_dist.fit(p)
fitted_prior = Beta(a, b)
pass
else:
return ('Method should be MM or MLE')
return fitted_prior
pass
def generate_image(self):
'''Provides histogram(s) with PDF curve(s)'''
# Setup plots
fig, ax = plt.subplots(figsize=(16, 6))
plt.subplots_adjust(bottom=.2)
ax.axes.set_title('Risk Distribution', fontsize=20)
# Format X axis
ax.axes.xaxis.set_major_formatter(StrMethodFormatter('${x:,.0f}'))
ax.axes.xaxis.set_tick_params(rotation=-45)
ax.set_ylabel('Frequency Histogram')
for tick in ax.axes.xaxis.get_major_ticks():
tick.label.set_horizontalalignment('left')
# Draw histrogram for each model
legend_labels = []
for name, model in self._input.items():
legend_labels.append(name)
plt.hist([model.export_results()['Risk']], bins=25, alpha=.3)
ax.legend(legend_labels, frameon=False)
# Min and Max post graphing
xmin, xmax = ax.get_xlim()
# Now draw twin axis a d style
tyax = plt.twinx(ax)
tyax.set_ylabel('PDF')
tyax.set_yticks([])
# Plot for each
for name, model in self._input.items():
risk = model.export_results()['Risk']
# Catch warnings as we're "fitting" with known shape parameters.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
beta_curve = beta(*beta.fit(risk))
space = np.linspace(0, xmax, 1000)
tyax.plot(space, beta_curve.pdf(space))
plt.margins(0)
return (fig, ax)
def maximum_likelihood_fit(data, loc=0, scale=1):
"""Estimate parameters from samples.
This a wrapper around scipy's maximum likelihood estimator to
estimate the parameters of a beta distribution from samples.
Parameters
----------
data : array-like, shape=[..., n_samples]
Data to estimate parameters from. Arrays of
different length may be passed.
loc : float
Location parameter of the distribution to estimate parameters
from. It is kept fixed during optimization.
Optional, default: 0.
scale : float
Scale parameter of the distribution to estimate parameters
from. It is kept fixed during optimization.
Optional, default: 1.
Returns
-------
parameter : array-like, shape=[..., 2]
Estimate of parameter obtained by maximum likelihood.
"""
data = gs.cast(data, gs.float32)
data = gs.to_ndarray(gs.where(data == 1., 1. - EPSILON, data),
to_ndim=2)
parameters = []
for sample in data:
param_a, param_b, _, _ = beta.fit(sample, floc=loc, fscale=scale)
parameters.append(gs.array([param_a, param_b]))
return parameters[0] if len(data) == 1 else gs.stack(parameters)
def pvalue(name, data):
p_value = []
total_number = len(name)
for i in range(len(name)):
sys.stdout.write('Sample tested: %d/' % (i) + str(total_number) +
' \r')
sys.stdout.flush()
ppvp = []
d1 = np.where(data[i] > 0, data[i], 0.0000000001)
d1 = np.where(d1 < 1, d1, 0.9999999999)
rl = []
for j in range(len(d1)):
if name[j].split('-')[0] == name[i].split('-')[0]:
rl.append(j)
d2 = []
for k in range(len(d1)):
if k in rl:
continue
else:
d2.append(d1[k])
try:
param = beta.fit(d2, floc=0, fscale=1)
except:
print d2
rv = beta(param[0], param[1], 0, 1)
for j in range(len(d1)):
if j != i:
ppvp.append(1 - rv.cdf(d1[j]))
else:
ppvp.append(1)
p_value.append(ppvp)
if save_label_files == 1:
np.savetxt(path + '/p_values.txt', np.array(p_value))
return np.array(p_value)
def fit(data: FloatIterable,
a: Optional[float] = None,
b: Optional[float] = None,
c: Optional[float] = None) -> 'PERT':
"""
Fit a PERT distribution to the data.
:param data: Iterable of data to fit to.
:param a: Optional fixed value for a.
:param b: Optional fixed value for b.
:param c: Optional fixed value for c.
"""
kwargs = {}
if a is not None:
kwargs['floc'] = a
if a is not None and c is not None:
kwargs['fscale'] = c - a
alpha, beta, loc, scale = beta_dist.fit(data=data, **kwargs)
a = a if a is not None else loc
c = c if c is not None else loc + scale
if b is None:
b_est_1 = a + (alpha * (c - a) - 1) / 4
b_est_2 = c - (beta * (c - a) - 1) / 4
b = (b_est_1 + b_est_2) / 2
return PERT(a=a, b=b, c=c)
def get_histogram(self):
# Fit a normal distribution to the data:
mu, std = norm.fit(self.data)
# Fit a beta distribution to the data
betaparams = beta.fit(self.data)
fig = plt.figure()
# plt.subplot(211)
# Plot the histogram.
plt.hist(self.data,
bins=2 * len(self.data),
normed=True,
alpha=0.6,
color='g')
# Plot the PDF.
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'k', linewidth=2)
b = beta.pdf(x, *betaparams)
plt.plot(x, b, 'r', linewidth=2)
title = "Normal fit: mu = %.2f, std = %.2f" % (mu, std)
plt.suptitle(title)
subtitle = "black: normal, red: beta"
plt.title(subtitle)
return fig
def confirmed_prior(save = False, name = 'data/distr/confirmedratio.csv'):
"""Get ratio of confirmed cases.
Args:
save (bool, optional): Whether to save the figure, defaultly not.
name (str, optional): Path to save the plot to.
"""
try:
return pd.read_csv(name)
except: pass
# get data
pop = population.countries()
df = src.get_data()
tests = testing.tests()
# group
iso3_iso2 = {'CZE':'CZ','SWE':'SE','POL':'PL','ITA':'IT'}
for country3 in df.iso_alpha_3.unique():
# get country population
country2 = iso3_iso2[country3]
country_pop = float(pop.population[pop.region == country2])
# normalize confirmed by tests
country_confirmed = df[df.iso_alpha_3 == country3].confirmed.apply(lambda c: c if c > 0 else 1)
country_tests = tests[tests.country == country3].tests.apply(lambda t: t if t > 0 else 1)
df.loc[df.iso_alpha_3 == country3,'ratio'] = (country_confirmed / country_tests).fillna(0)
df['ratio'] = df.ratio.apply(lambda r: r if r < 1 else 1 - 1e-6)
df['ratio'] = df.ratio.apply(lambda r: r if r > 0 else 1e-6)
df[df.iso_alpha_3 == country3]['tests'] = country_tests
df[df.iso_alpha_3 == country3]['confirmed'] = country_confirmed
df = df[['iso_alpha_3','date','confirmed','tests','ratio']]
confirmed_fit = beta.fit(df.ratio, floc = 0, fscale = 1)
# save
if save: df.to_csv(name, index = False)
return df
def model_df(p_df, ax, title=''):
# construct prior
cov_cutoff = 100
hi_df = p_df\
.query('(sense + antisense) > %i' %cov_cutoff)\
.assign(percentage_sense = lambda d: d.sense/(d.sense + d.antisense))
fitted_params = beta.fit(data=hi_df.percentage_sense.values,
floc=0,
fscale=1)
print(fitted_params)
alpha0, beta0, loc, scale = fitted_params
hist = True
bins = 10
sns.distplot(hi_df.percentage_sense,
label='High count repeats (>%i fragments)' % cov_cutoff,
ax=ax,
hist_kws={'alpha': 0.5},
bins=bins,
hist=hist)
ls = np.linspace(0, 1, 1000)
ax.plot(ls,
beta.pdf(ls, alpha0, beta0, loc, scale),
label='Fitted beta-binomial')
#sns.distplot(np.random.beta(alpha0, beta0, size=hi_df.shape[0]))
ax.legend(frameon=False, bbox_to_anchor=(0.6, 1.1))
ax.set_xlabel('Propotion of sense strand fragments')
ax.set_ylabel('Density')
ax.set_xlim(0, 1)
ax.set_title(title, fontsize=15)
sns.despine()
return alpha0, beta0
def get_beta_percentile_confidence(self, conf=0.60):
'''
I am conf percent sure that I will be there in :return: minutes or less
invert conf for "minutes or more". eg 0.10== 90 percent sure it'll take at least...
'''
betaparams = beta.fit(self.data)
return beta.ppf(conf, *betaparams)
def define_correction_function(top_pvalues_perm, cis_mode):
#Always try to use the MLE estimator, new default to 10 permutations.
#If the MLE estimator fails we go back to the cruder estimation of the beta distribution.
offset = (np.finfo(np.double).tiny * 100)
##Replace zero's value with smallest number not 0.
top_pvalues_perm[top_pvalues_perm == 0] = offset
##Replace highest value with highest number not 1.
top_pvalues_perm[top_pvalues_perm == 1] = 1 - offset
try:
alpha_para, beta_para, loc, fscale = beta.fit(top_pvalues_perm,
floc=0,
fscale=1)
except (scipy.stats._continuous_distns.FitSolverError):
alpha_para, beta_para = estimate_beta_function_paras(top_pvalues_perm)
except (scipy.stats._continuous_distns.FitDataError):
alpha_para, beta_para = estimate_beta_function_paras(top_pvalues_perm)
if (cis_mode):
if (alpha_para < BETA_SHAPE1_MIN or alpha_para > BETA_SHAPE1_MAX
or alpha_para < BETA_SHAPE2_MIN_CIS
or alpha_para > BETA_SHAPE2_MAX_CIS):
alpha_para, beta_para = estimate_beta_function_paras(
top_pvalues_perm)
### If pvalues become more significant after multiple testing correction we put them back to the orignal test Pvalue in a seperate step.
else:
if (alpha_para < BETA_SHAPE1_MIN or alpha_para > BETA_SHAPE1_MAX
or alpha_para < BETA_SHAPE2_MIN_TRANS
or alpha_para > BETA_SHAPE2_MAX_TRANS):
alpha_para, beta_para = estimate_beta_function_paras(
top_pvalues_perm)
beta_dist = scipy.stats.beta(alpha_para, beta_para)
correction_function = lambda x: beta_dist.cdf(x)
#Would be good to replace 0 with minimal double value of python.
return [correction_function, alpha_para, beta_para]
def test_draw_samples_non_mock(self, plot=False):
# Also make sure the non-mock sampler works
dtype = np.float32
num_samples = 100000
a = np.array([2])
b = np.array([5])
rv_shape = (1, )
a_mx = add_sample_dimension(mx.nd, mx.nd.array(a, dtype=dtype))
b_mx = add_sample_dimension(mx.nd, mx.nd.array(b, dtype=dtype))
rand_gen = None
var = Beta.define_variable(shape=rv_shape,
rand_gen=rand_gen,
dtype=dtype).factor
variables = {var.alpha.uuid: a_mx, var.beta.uuid: b_mx}
rv_samples_rt = var.draw_samples(F=mx.nd,
variables=variables,
num_samples=num_samples)
assert array_has_samples(mx.nd, rv_samples_rt)
assert get_num_samples(mx.nd, rv_samples_rt) == num_samples
assert rv_samples_rt.dtype == dtype
if plot:
plot_univariate(samples=rv_samples_rt, dist=beta, a=a[0], b=b[0])
a_est, b_est, _, _ = beta.fit(rv_samples_rt.asnumpy().ravel())
a_tol = 0.2
b_tol = 0.2
assert np.abs(a[0] - a_est) < a_tol
assert np.abs(b[0] - b_est) < b_tol
def maximum_likelihood_fit(self, data, loc=0, scale=1):
"""Estimate parameters from samples.
This a wrapper around scipy's maximum likelihood estimator to
estimate the parameters of a beta distribution from samples.
Parameters
----------
data : array-like, shape=[n_distributions, n_samples]
the data to estimate parameters from. Arrays of
different length may be passed.
loc : float, optional
the location parameter of the distribution to estimate parameters
from. It is kept fixed during optimization
default: 0
scale : float, optional
the scale parameter of the distribution to estimate parameters
from. It is kept fixed during optimization
default: 1
Returns
-------
parameter : array-like, shape=[n_samples, 2]
"""
data = gs.to_ndarray(
gs.where(data == 1., 1 - EPSILON, data), to_ndim=2)
parameters = []
for sample in data:
param_a, param_b, _, _ = beta.fit(sample, floc=loc, fscale=scale)
parameters.append(gs.array([param_a, param_b]))
return parameters[0] if len(data) == 1 else gs.stack(parameters)
def reference_sim(self, A, classified,labels):
num_centers = len(set(labels))
small = .0000000000001
ideal_A = np.zeros([A.shape[0],A.shape[1]])
for i in range(0,len(labels)):
for j in range(0,i+1):
if labels[i] == labels[j]:
ideal_A[i,j] = 1
ideal_A[j,i] = 1
pred_pos = A[ideal_A ==1]
pred_neg = A[ideal_A ==0]
pos_a,pos_b,pos_loc, pos_scale= beta.fit(pred_pos)
neg_a,neg_b,neg_loc, neg_scale= beta.fit(pred_neg)
fits = []
#Fit comparison iwth more than 1 clust
for sim in range(0, 50):
simulated_mat = np.ones([A.shape[0],A.shape[1]])
for i in range(0,len(labels)):
for j in range(0,i):
if ideal_A[i,j] ==0:
simulated_mat[i,j] = simulated_mat[j,i]= beta.rvs(max(neg_a,small), max(small,neg_b), loc=neg_loc,scale =neg_scale)
else:
simulated_mat[i, j ] = simulated_mat[j, i ] = beta.rvs(max(pos_a,small), max(small,pos_b), loc=pos_loc,scale =pos_scale)
self.one_clust_test = False
whereAreNaNs = np.isnan(simulated_mat)
simulated_mat[whereAreNaNs] = 0
self.fit(simulated_mat)
#print simulated_mat
fits.append(self.gap_stat_)
multi_fit = np.mean(fits)
fits_one = []
pos_a,pos_b,pos_loc, pos_scale= beta.fit(A)
for sim in range(0, 50):
simulated_mat = np.ones([A.shape[0],A.shape[1]])
for i in range(0,len(labels)):
for j in range(0,i):
simulated_mat[i,j] = simulated_mat[j,i]= beta.rvs(max(small,pos_a), max(small,pos_b), loc=pos_loc,scale =pos_scale)
whereAreNaNs = np.isnan(simulated_mat)
simulated_mat[whereAreNaNs] = 0
e_vals, e_vecs = np.linalg.eigh(simulated_mat)
#2. Get Reverse Sorted Order - largest to smallest
e_order = np.argsort(e_vals)[::-1]
self.one_clust_fit_alt(e_vecs,e_order)
fits_one.append(self.gap_stat_)
one_fit = np.mean(fits_one)
return multi_fit, one_fit
def fit_beta(data):
EPSILON = 1e-3
data = [EPSILON if d == 0 else 1 - EPSILON if d == 1 else d
for d in data] # make sure no data points EQUAL one of the bounds
if len(set(data)) == 1: # make sure not all data points are identical
data = [d + random.normalvariate(0, 1e-6) for d in data]
params = beta.fit(data, floc=0, fscale=1)
return params[0], params[1]
def gen_beta(data):
# Param 0 is alpha
# Param 1 is b1
# Param 2 is loc
data = data[~np.isnan(data)]
data = np.array([.01 if i == 0 else .99 if i == 1 else i for i in data])
beta_params = beta.fit(data, floc=0., fscale=1.)
return beta_params[0], beta_params[1]
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 beta_fit(data, col):
plt.hist(data[col], bins=10000, density=True, alpha=0.6, color='g')
a, b, loc, scale = beta.fit(data[col])
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 10000)
# plt.title("Beta fitting of "+str(col))
# plt.plot(x, p, 'r', linewidth=2)
# plt.show()
return x, a, b, loc, scale
def run_beta_fit(cadd_trset, mnp_cadd_trset, gerp_trset):
'''
from scipy import stats
import numpy as np
import matplotlib.pylab as plt
# create some normal random noisy data
ser = 50*np.random.rand() * np.random.normal(10, 10, 100) + 20
# plot normed histogram
plt.hist(ser, normed=True)
# find minimum and maximum of xticks, so we know
# where we should compute theoretical distribution
xt = plt.xticks()[0]
xmin, xmax = min(xt), max(xt)
lnspc = np.linspace(xmin, xmax, len(ser))
ab,bb,cb,db = stats.beta.fit(ser)
pdf_beta = stats.beta.pdf(lnspc, ab, bb,cb, db)
plt.plot(lnspc, pdf_beta, label="Beta")
plt.show()
'''
cadd_trset_param = {}
for aaconv in cadd_trset.keys():
a,b,loc2,scale2 = beta.fit(cadd_trset[aaconv])
mean2 = beta.mean(a,b,loc2,scale2)
cadd_trset_param[aaconv] = [a,b,loc2,scale2,mean2]
mnp_cadd_trset_param = {}
for aaconv in mnp_cadd_trset.keys():
a,b,loc2,scale2 = beta.fit(mnp_cadd_trset[aaconv])
mean2 = beta.mean(a,b,loc2,scale2)
mnp_cadd_trset_param[aaconv] = [a,b,loc2,scale2,mean2]
gerp_trset_param = {}
for aaconv in gerp_trset.keys():
a,b,loc2,scale2 = beta.fit(gerp_trset[aaconv])
mean2 = beta.mean(a,b,loc2,scale2)
gerp_trset_param[aaconv] = [a,b,loc2,scale2,mean2]
return cadd_trset_param, mnp_cadd_trset_param, gerp_trset_param
def beta_dist_estimator(data):
if not data.empty:
x = data.Age.value_counts(dropna=False).sort_index()
z = x / np.sum(x)
a1, b1, loc1, scale1 = beta.fit(z, floc=0, fscale=1)
return a1, b1, loc1, scale1
def plot():
CURRENT_PATH = os.path.dirname(os.path.realpath(__file__))
# Final model with exponential hyperprior
MODEL_PATH = os.path.join(CURRENT_PATH, "..", "bayes_cv_prune",
"stan_models", "exp_new.stan")
model = BayesStanPruner(MODEL_PATH, seed=0).load()
# Simulated sets of accuracy values
A0 = [0.4, 0.5]
A1 = [0.80, 0.82]
post_sample0 = model.fit_predict(A0)
post_sample1 = model.fit_predict(A1)
x = np.linspace(0, 1, 250)
a, b, _, _ = beta.fit(A0, floc=0, fscale=1)
ml_estimate0 = beta.pdf(x, a, b)
a, b, _, _ = beta.fit(A1, floc=0, fscale=1)
ml_estimate1 = beta.pdf(x, a, b)
# Plot
sns.set(context="paper",
style="whitegrid",
font="STIXGeneral",
font_scale=1.25)
bins = np.linspace(0, 1, 41)
f, axes = plt.subplots(1, 2, figsize=(6.5, 3))
axes[0].hist(post_sample0, bins=bins, density=True, label="Post. pred.")
axes[0].plot(x, ml_estimate0, '-k', label="Beta ML fit")
axes[0].set_title("A = {0.4, 0.5}")
axes[0].legend()
axes[1].hist(post_sample1, bins=bins, density=True, label="Post. pred.")
axes[1].plot(x, ml_estimate1, '-k', label="Beta ML fit")
axes[1].set_title("A = {0.80, 0.82}")
axes[1].legend()
plt.subplots_adjust(left=0.065, bottom=0.095, top=0.9, right=0.975)
plt.show()
def EI():
"""Get distributions for parameter c, connection E-I."""
# seed
np.random.seed(seed=12345)
# draw from incubation period
pars = incubation.continuous()['gamma']
draws = gamma.rvs(*pars, size = 1000000, random_state = 12345)
# fit beta to 1/draw
samples = 1 / draws
samples = samples[(samples > 0) & (samples < 1)]
return {'x': samples,
'beta': beta.fit(samples),
'gamma': gamma.fit(samples, loc = .2, scale = 10)}
def fit_beta(table, xlims=(-2, 0.5)):
"""Returns fit of Beta Distribution to a given [Fe/H] table
See: fit_gaussian()
"""
z_sort = np.sort(table['feh'])
i_0 = np.searchsorted(z_sort, xlims[0])
i_1 = np.searchsorted(z_sort, xlims[1])
loc = xlims[0]
scale = xlims[1] - xlims[0]
a, b, loc, scale = beta.fit(z_sort[i_0:i_1], floc=loc, fscale=scale)
return a, b, loc, scale
plt.figure(figsize=(10, 7))
plt.axvline(mle, linestyle ="--")
line1, = plt.plot(possible_thetas, likelihoods)
bins = [x/100 for x in range(100)]
counts, bins = np.histogram(infections_rates, bins=bins)
counts = counts / counts.sum()
line2, = plt.plot(bins[:-1], counts)
plt.xlabel("Theta")
plt.title("Evidence vs Historical Infection Rates")
plt.legend((line1, line2), ('Likelihood of Theta with new evidence', 'Frequency of Theta in last 100 months')
, loc = 'upper left')
plt.show()
# Model the data with a beta function
prior_a, prior_b = beta.fit(infections_rates, floc = 0, fscale = 1)[0:2] # Fit data to find a & b for the beta dist.
prior = beta(prior_a, prior_b)
prior_samples = prior.rvs(10000) # Sample from the prior
beta_sample_counts, bins = np.histogram(prior_samples, bins)
total = beta_sample_counts.sum()
beta_sample_counts = [x / total for x in beta_sample_counts]
plt.figure(figsize=(10, 7))
line1, = plt.plot(bins[:-1], beta_sample_counts)
hist_rates, bins = np.histogram(infections_rates, bins)
total = hist_rates.sum()
hist_rates = [x/total for x in hist_rates]
line2, = plt.plot(bins[:-1], hist_rates)
numpy.set_printoptions(linewidth=1000000)
probs = []
pvals = []
pyplot.figure(figsize=(15,10))
for i, data in enumerate(bin_data):
print i
tdata = numpy.array(data)
params = beta_dist.fit(tdata, floc=0)
(alpha, beta, floc, fshape) = params
print alpha, beta
vals = numpy.arange(0, strand_length + 1)
fit_hist = numpy.array([beta_binom(val, strand_length, alpha, beta) for val in vals])
#data_bin_edges = numpy.linspace(0.0, 1.0, num=strand_length + 1, endpoint=True)
data_bin_edges = numpy.linspace(-0.5, strand_length + 0.5, num=strand_length + 2, endpoint=True) / float(strand_length)
#print data_bin_edges
# Copyright 2015, Chen Sun (bbsunchen at outlook.com)
from sys import argv
from scipy.stats import beta
frequence_list = []
with open(argv[1]) as input_file:
for line in input_file:
line = line.strip()
if line.startswith('rs#'):
continue
columns = line.split(' ')
#print line
ref_freq = float(columns[11])
oth_freq = float(columns[14])
if ref_freq >= 0.05 and ref_freq <= 0.95:
frequence_list.append(ref_freq)
if oth_freq >= 0.05 and oth_freq <= 0.95:
frequence_list.append(oth_freq)
# direct fit Beta distribution
#print beta.fit(frequence_list)
# fit Beta distribution with frequency [0.05, 0.95], more precise
a,b,l,s=beta.fit(frequence_list, floc=0.04999999999999999, fscale=0.9000000000000000)
#print str(a)+'\t'+str(b)
print '{}\t{}'.format(a,b)
# fit Beta distribution with some infer
#print beta.fit(frequence_list, floc=0, fscale=1)
#analysis of mccarthy's data
import numpy as np
import csv
import pylab as plt
import pymc as pm
from sklearn import mixture
import sys
from scipy.stats import beta
def gaussian(x, mu, sig):
return (1/(sig*np.sqrt(2*np.pi)))*np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))
filename = "C:\Users\leon\Documents\Data\McCarthys.dat"
raw_data = list(csv.reader(open(filename, 'rb')))
data = np.zeros((256,2))
for idx,line in enumerate(raw_data):
data[idx, :] = line[0].split()
master_data = np.array([])
for line in data:
new_data = np.repeat(line[0], line[1])
master_data = np.append(master_data, new_data)
master_data = master_data[:, None]
#clf = mixture.GMM(n_components=2, )
#clf.fit(master_data)
#plt.hist(master_data, 100, normed = True)
#plt.plot(gaussian(np.linspace(0,50,100), clf.means_[0], clf.covars_[0]))
#plt.plot(gaussian(np.linspace(0,50,100), clf.means_[1], clf.covars_[1]))
sys.exit()
[alpha, beta, loc, scale] = beta.fit(master_data)
smoothMeth = regionDict[key]["SMOOTHED"]
rawScores = []
smoothScores = []
for r,s in zip(rawMeth, smoothMeth):
rawScores.append(float(r['score'])/100)
smScore = float(s['score'])/100
if smScore == 1:
smScore = 0.9999999999
if smScore == 0:
smScore = 0.0000000001
smoothScores.append(smScore)
print(smoothScores)
if np.var(smoothScores) == 0:
break
else:
fit = beta.fit(smoothScores, floc=0, fscale=1)
alphas.append(fit[0])
betas.append(fit[1])
statuses.append(status)
fig = plt.figure()
cols = {1:'b', -1:'r', 0:'k'}
for a,b,s in zip(alphas, betas, statuses):
plt.plot(a,b, cols[s]+'o',markersize = 2)
plt.savefig('beta_plot.pdf', bbox_inches = 'tight')
def vplik(old, imps, cclass): # p = multiprocessing.Pool(multiprocessing.cpu_count()) preimage = [(old,imps,random.randint(0,1000000)) for i in xrange(PLIK_REPS)] # print preimage image = np.array(P.map(rboot,preimage)) return beta(*beta.fit(image[:,0]))
bin_step = 100.0/n_bins
bin_edges = numpy.arange(0.0, 100.0 + bin_step, bin_step)
print bin_edges
bin_data = [[] for _ in range(len(bin_edges) - 1)]
for i in range(len(x)):
n_hb = y[i]
val = x[i]
for j in range(len(bin_data)):
if (val <= bin_edges[j+1]) and (val > bin_edges[j]):
bin_data[j].append(n_hb)
for i, data in enumerate(bin_data):
tdata = numpy.array(data)
params = beta.fit(tdata, fscale=1)
print i, params[0], params[1], params[2], params[3]
#print tdata
if correct < 50:
continue
if (incorrect > 0) and (correct > 0):
print name,len(times[name])
print correct,incorrect
print np.mean(correct_times),np.mean(incorrect_times)
#print sum([1 for c in correct_times if c >= min(incorrect_times)])/float(len(correct_times))
#print
max_time = max(max(correct_times),max(incorrect_times))
min_time = min(min(correct_times),min(incorrect_times))
data = correct_times
data = [(t-min_time)/float(max_time-min_time) for t in data]
#print data
a,b,lower,scale = beta.fit(data)
#print a,b,lower,scale
#print
#print beta.cdf(0.8,a,b)
#----------------Fit using moments----------------
mean=np.mean(data)
var=np.var(data,ddof=1)
alpha1=mean**2*(1-mean)/var-mean
beta1=alpha1*(1-mean)/mean
print beta.cdf((incorrect_times[-1]-min_time)/(max_time-min_time),alpha1,beta1)
print
#break
#print correct_times
import numpy as np
from numpy import random as rnd
from scipy.stats import t
from scipy.stats import beta
import seaborn as sns
import matplotlib.pyplot as plt
### Parameters ###
nu = 3 # arbitrary choice
sigma = 2 # arbitrary choice
### Simulate Values ###
draws1 = rnd.standard_t(nu, size = 1.e6) # draw one million Y values
draws2 = t.cdf(draws1, df = nu) # classic PIT (F_Y(Y) is standard uniform)
draws3 = t.cdf(draws1 / sigma, df = nu) # compute one million X values based on Y values
alpha_fit, beta_fit, loc_fit, scale_fit = beta.fit(draws3) # determine best-fitted beta
### Plot KDEs and CDF of Best-Fitted Beta PDF ###
kdeFig = plt.figure() # start figure
sns.set("talk") # set seaborn style to "talk" --> increase font size
# add all KDEs with plot labels in LaTeX (hence the use of raw strings)
sns.kdeplot(draws1, shade = True, clip = (-3, 3), label = r'KDE for $Y \sim t_3$')
sns.kdeplot(draws2, shade = True, clip = (-3, 3), label = r'KDE for $F_Y(Y)$')
sns.kdeplot(draws3, shade = True, clip = (-3, 3), label = r'KDE for $X$ with $\sigma = 2$')
# add pdf for best-fitted beta with plot labels in LaTeX (hence the use of raw strings)
x = np.linspace(-3,3, num = 1000) # create 1000 values between -3 and +3
y = beta.pdf(x, a = alpha_fit, b = beta_fit, loc = loc_fit, scale = scale_fit) # f(x)
plt.plot(x, y, label = r'PDF for Best-Fitted $B(\alpha, \beta)$')
# title & legend
plt.title('Kernel Density Estimation')
plt.legend(loc='upper left')
