def give_page(self):
#get product
product_i = random.randint(0, len(productids)-1)
product = productids[product_i]
#get price
prod_price_dist = beta.rvs(self.product_price_alpha[product_i], self.product_price_beta[product_i])*(prices/50.0)
price_i = np.argmax(prod_price_dist)
price = prices[price_i]
#get header
header_dist = beta.rvs(self.header_alpha, self.header_beta)
header_i = np.argmax(header_dist)
header = headers[header_i]
#get adtype
adtype_dist = beta.rvs(self.adtype_alpha, self.adtype_beta)
adtype_i = np.argmax(adtype_dist)
adtype = adtypes[adtype_i]
#get color
color_dist = beta.rvs(self.color_alpha, self.color_beta)
color_i = np.argmax(color_dist)
color = colors[color_i]
return {'header': header, 'adtype': adtype, 'color': color, 'productid': product, 'price': price}, [header_i, adtype_i, color_i, product_i, price_i]
def give_page(self):
#get header
header_dist = [beta.rvs(self.header_alpha[i], self.header_beta[i]) for i in range(len(headers))]
header_i = header_dist.index(max(header_dist))
header = headers[header_i]
#get adtype
adtype_dist = [beta.rvs(self.adtype_alpha[i], self.adtype_beta[i]) for i in range(len(adtypes))]
adtype_i = adtype_dist.index(max(adtype_dist))
adtype = adtypes[adtype_i]
#get color
color_dist = [beta.rvs(self.color_alpha[i], self.color_beta[i]) for i in range(len(colors))]
color_i = color_dist.index(max(color_dist))
color = colors[color_i]
#get productid
product_dist = [beta.rvs(self.product_alpha[i], self.product_beta[i]) for i in range(len(productids))]
product_i = product_dist.index(max(product_dist))
product = productids[product_i]
#get price
price_dist = [beta.rvs(self.price_alpha[i], self.price_beta[i]) for i in range(len(prices))]*(prices/50.0)
price_i = np.argmax(price_dist)
price = prices[price_i]
return {'header': header, 'adtype': adtype, 'color': color, 'productid': product, 'price': price}, [header_i, adtype_i, color_i, product_i, price_i]
def give_page(self, context):
product_alpha, product_beta, product_price_alpha, product_price_beta, header_alpha, header_beta, adtype_alpha, adtype_beta, color_alpha, color_beta = self.get_context_ab(context)
header_dist = [beta.rvs(header_alpha[i], header_beta[i]) for i in range(len(headers))]
header_i = header_dist.index(max(header_dist))
header = headers[header_i]
#get adtype
adtype_dist = [beta.rvs(adtype_alpha[i], adtype_beta[i]) for i in range(len(adtypes))]
adtype_i = adtype_dist.index(max(adtype_dist))
adtype = adtypes[adtype_i]
#get color
color_dist = [beta.rvs(color_alpha[i], color_beta[i]) for i in range(len(colors))]
color_i = color_dist.index(max(color_dist))
color = colors[color_i]
#get productid
product_dist = [beta.rvs(product_alpha[i], product_beta[i]) for i in range(len(productids))]
product_i = product_dist.index(max(product_dist))
product = productids[product_i]
#get price
price_alpha = product_price_alpha[product_i]
price_beta = product_price_beta[product_i]
price_dist = [beta.rvs(price_alpha[i], price_beta[i]) for i in range(len(prices))]
price_i = price_dist.index(max(price_dist))
price = prices[price_i]
return {'header': header, 'adtype': adtype, 'color': color, 'productid': product, 'price': price}, [product_i, price_i, header_i, adtype_i, color_i]
def get_issue_lift(all_visits, top_issues, half_life=90, min_lift=1):
"""
get_issue_lift uses the bureau visit data and calculate the lift between issues
i.e. if a client ask for help for issue A, is he more likely to ask for help for issue B in the future?
mathematically it is defined as P(B|A)/P(B). If the lift is > 1 than A is a good indicator that of
future need of B. The half_life allows for aging i.e. the importance of issue A as a predictor of B
halves every x days.
Return a dict {(issue_A, issue_B):lift}
"""
number_of_clients = len(all_visits)
# Remove "Other" from top_issues
for i in xrange(len(top_issues)):
if top_issues[i][0]=="Other":
del top_issues[i]
break
# Save time and only calculate for the top issues
top_issue_set = frozenset(map(operator.itemgetter(0), top_issues))
all_visits = filter_visits_by_issues(all_visits, top_issue_set)
# Count the number of clients affected by each of the top issue
issue_count = {issue:sum(any(issue in visit_issues for visit_issues in client_visits.itervalues())
for client_visits in all_visits.itervalues()) for issue in top_issue_set}
# Iterate through the clients and find all A->B pairs and their ages
# for each A->B pair only take the one that is closest together
decay = 0.5**(1.0/half_life)
lift_dict = defaultdict(float)
for client_id, client_visits in all_visits.iteritems():
number_of_visits = len(client_visits)
visit_days = sorted(client_visits.keys())
client_pairs = {}
for i in xrange(number_of_visits):
current_date = visit_days[i]
current_issues = client_visits[current_date]
for issue_A in current_issues:
for future_date in visit_days[i+1:]:
gap = (future_date-current_date).days
future_issues = client_visits[future_date]
for issue_B in future_issues:
if issue_A!=issue_B:
client_pairs[(issue_A,issue_B)] = min(gap, client_pairs.get((issue_A,issue_B),1e100))
# Turn the gaps into weights and add to the lift dict
for pair,gap in client_pairs.iteritems():
lift_dict[pair] += decay**gap
# turn the appearance count into lifts
for (issue_A,issue_B),count in lift_dict.iteritems():
p_issues_A_B = beta.rvs(1+count,1+issue_count[issue_B]-count,size=10000)
p_issue_A = beta.rvs(1+issue_count[issue_A], 1+number_of_clients-issue_count[issue_A],size=10000)
lift_dict[(issue_A, issue_B)] = np.median((p_issues_A_B-p_issue_A)/p_issue_A)+1
# Filter out the pairs below the threshold
lift_dict = {pair:lift for pair,lift in lift_dict.iteritems() if lift>min_lift}
return lift_dict
def sample_posteriors( dataA, dataB ): samplesA = beta.rvs( ALPHA_ + dataA['conversions'], BETA_ + dataA['total'] - dataA['conversions'], size = 1000) samplesB = beta.rvs( ALPHA_ + dataB['conversions'], BETA_ + dataB['total'] - dataB['conversions'], size = 1000) return samplesA, samplesB
def simulation(a, b, c, d, samples=100000, prob_diff=0.0, significance_level=0.95,
ensure_convergence=False, ensure_samples=10000000, ensure_radius=0.005):
"""For what it's worth... Not as accurate or efficient as the integration,
but still fun to play with"""
difference = beta.rvs(a + 1, c + 1, size=samples) - beta.rvs(b + 1, d + 1, size=samples)
successes = (difference > prob_diff).sum()
result = successes / samples
if ensure_convergence and abs(result - significance_level) < ensure_radius:
result = test(a, b, c, d, samples=ensure_samples, prob_diff=prob_diff, ensure_convergence=False)
return result
def parse_distribution(params, dist=False):
distribName = params[0]
params[1], params[2] = float(params[1]), float(params[2])
if distribName == 'norm':
if len(params)!=3:
displaymessage("Normal distribution takes two parameters",end_execution=False)
out=1
loc, scale = params[1], params[2]
if dist:
out = lambda x : norm.rvs(loc, scale)
else:
loop_counter=0;
out=norm.rvs(loc,scale)
while (out<0):
out=norm.rvs(loc,scale)
loop_counter+=1
if loop_counter>100:
displaymessage("Distribution too far in the negative. Set to default 1.",end_execution=False)
out=1
break
elif distribName == 'uniform': #constant between loc and loc+scale
if len(params)!=3:
displaymessage("Uniform distribution takes two parameters",end_execution=False)
out=1
loc, scale = params[1], params[2]
if (loc+scale <= 0):
displaymessage("Distribution lies entirely to the left of the y-axis. Changed to default value 1.",end_execution=False)
out=1
elif loc<0:
displaymessage("Uniform distribution takes negative values.",end_execution=False)
loc = max(loc,0)
else:
if dist:
out = lambda x : uniform.rvs(loc, scale)
else:
out = uniform.rvs(loc, scale)
elif distribName == 'beta':
if len(params)!=5:
displaymessage("Normal distribution takes four parameters",end_execution=False)
out=1
a, b, loc, scale = params[1], params[2], float(params[3]), float(params[4])
if dist:
out = lambda x : beta.rvs(a, b, loc, scale)
else:
out = beta.rvs(a, b, loc, scale)
return out
def sample_dist_beta(nsample,
median,
ll_cl,
up_cl,
blur=50,
assume='norm',
func='',
func_args=[3],
source=0):
'''
Use beta distribution to add skew.
'''
window = up_cl - ll_cl
sd_s = (window) / 2
rate = (median - ll_cl) / window
print(rate)
t = np.pi / 2
a = np.sin(rate * t) * blur
b = np.cos(rate * t) * blur
f = beta.rvs(a, b, size=nsample)
if not source:
f = f * window + ll_cl
if func:
f = [func(x, *func_args) for x in f]
return f
def _parse_random_params(prior_a, prior_c, prior_b, prior_d):
"""Interprets parameters as random variables of parameters.
Args:
prior_a,prior_c,prior_b (tuple of floats): Parameters of Beta random variables.
prior_d (tuple of floats): Parameters of Uniform random variable.
Returns:
(tuple (4) of floats): Parameters a,c,b,d.
"""
# random draws
a = beta.rvs(*prior_a, size=1)[0]
c = beta.rvs(*prior_c, size=1)[0]
b = beta.rvs(*prior_b, size=1)[0]
d = uniform.rvs(*prior_d, size=1)[0]
# return parameters
return a, c, b, d
def generate_samples(dist, curr_params, n):
"""
This method generates a sample from a given distribution with
given parameters
:param dist: str
Type of distribution from which to draw a sample
:param curr_params: tuple[float, float]
Parameters for a distribution
:param n: int
Length of generated sample
:return: tuple[float, float]
A pair (quantile skewness, quantile kurtosis) which can be viewed
as a point and act as a representation of the generated sample on a Pearson System
"""
if dist == 'beta':
a, b = curr_params
curr_sample = beta.rvs(a, b, size=n)
elif dist == 'betaprime':
a, b = curr_params
curr_sample = betaprime.rvs(a, b, size=n)
else:
return (None, None)
curr_sample.sort()
curr_e = [curr_sample[int(j * n)] for j in OCTILES]
curr_s = (curr_e[5] - 2 * curr_e[3] + curr_e[1]) / (curr_e[5] - curr_e[1])
curr_t = (curr_e[6] - curr_e[4] + curr_e[2] - curr_e[0]) / (curr_e[5] -
curr_e[1])
return (curr_s, curr_t)
def FisherDistribution2(k, n, m, ele=0, azi=0):
# m is the number of dimensions
b = (-2 * k + np.sqrt(4 * k**2 + (m - 1)**2)) / (m - 1)
x0 = (1 - b) / (1 + b)
c = k * x0 + (m - 1) * np.log(1 - x0**2)
numvec = 0
cartesian_sample = np.zeros((n, m))
while numvec < n:
Z = beta.rvs((m - 1) / 2, (m - 1) / 2, size=1)
U = np.random.uniform(0, 1, 1)
W = (1 - (1 + b) * Z) / (1 - (1 - b) * Z)
if k * W + (m - 1) * np.log(1 - x0 * W) - c < np.log(U):
continue
else:
theta = 2 * np.pi * np.random.uniform(0, 1, 1)[0]
V = np.array([np.cos(theta), np.sin(theta)]) # 2d vector
X = np.concatenate((np.sqrt(1 - W**2) * V, W))
cartesian_sample[numvec, :] = X
numvec += 1 # count the free rows in cartesian, when fully filled - quit
if ele == 0 and azi == 0:
pass
else:
cartesian_sample = cartesian_sample.T
transformed = Rz(azi, Ry(ele, cartesian_sample))
cartesian_sample = transformed.T
return cartesian_sample
def update_current_sample(n_clicks):
# first sample from current posterior beta distribution
p = beta.rvs(a, b, size=1)[0]
print("Sample: " + str(p))
return json.dumps({'sample':p})
def generate_dataset(num_points, true_k, dim=2, verbose=False):
"""
Generates num_points datapoints with true_k clusters
:param num_points: Number of data points
:param true_k: Number of clusters in the generated data
:param dim: dimensionality of the data space
:param verbose: print stuff
:return: dataset in [num_points,dim]
"""
# Construct a dataset
true_z = [i % true_k for i in range(num_points)]
# I like to sample from the peaked beta distro to get more interpretable results. But feel free to sample
# from the random uniform
if False:
probs_per_cluster = np.random.rand(dim, true_k)
else:
probs_per_cluster = beta.rvs(0.5, 0.5, size=(true_k, dim))
if verbose:
print('Probabilities for the generated data')
for i, trueprobs in enumerate(probs_per_cluster):
print('Cluster %3i with %s' %
(i, ' - '.join(['%5.1f' % prob for prob in trueprobs])))
print('-' * 30)
probs = probs_per_cluster[true_z, :]
# Sample from the probabilities
data = (probs > np.random.rand(num_points, dim)).astype(np.float32)
return data
def plotter(being: str, name: str, axis, data: dict):
# Color-blind friendly palette
colors = {
'forest': '#d7191c',
'network': '#fdae61',
'random': '#abd9e9',
'humans': '#2c7bb6',
}
# Beta Distribution generation from the measured predictive-quality
ypred = beta.rvs(
1 + data[f'total {name}'][0] * data[name][0],
1 + data[f'total {name}'][0] * (1 - data[name][0]),
size=4000,
)
# plot with Seaborn that binnarizes automatically
sns.distplot(100 * ypred,
bins=None,
hist=True,
kde=False,
label=f'{being}',
ax=axis,
color=colors[being])
return
def search(self, state, color, depth, subtree):
"""
path: if you choose 19 from enable=[13,19,20,21] (choose enable[1])
and then opponent choose 32 from enable=[14,24,32,53], (enable[2])
the path is [1,2]
"""
enable = reversi.getPossiblePoints(state, color)
if depth == self.depth + 1:
# no thinking (simulate)
if len(enable) == 0:
return self.simulate(state, 65, color)
row, line = random.choice(enable)
return self.simulate(state, row * 8 + line, color)
if len(enable) == 0:
return self.search(state, color ^ 1, depth + 1, subtree)
if len(subtree) == 0:
# first visit
subtree.extend([[0, 0, []] for _ in enable])
wins = np.array([node[0] for node in subtree])
loses = np.array([node[1] for node in subtree])
values = beta.rvs(wins + 1, loses + 1)
choice = values.argmax()
row, line = enable[choice]
reversi.putStone(state, row, line, color)
r = self.search(state, color ^ 1, depth + 1, subtree[choice][2])
if r == color:
subtree[choice][0] += 1
else:
subtree[choice][1] += 1
return r
def get_particle_from_state(self, state, obs):
"""
Returns a particle from this state, as well as the log_density of this particle
"""
sample_means = beta.rvs(state['successes'], state['failures'])
log_density = np.sum(beta.logpdf(sample_means, state['successes'], state['failures']))
return sample_means, log_density
def lichess_computer(self, difficulty=10):
url = 'https://www.chessdb.cn/cdb.php?action=queryall&board=' + self._fen() + '&json=1'
r = requests.get(url)
try:
moves = print_json(r.text)['moves'] #The moves are already ordered based on their score
board_move = []
for move in moves:
board_move.append(move['uci'])
if len(list(self._board.legal_moves)) == len(board_move): #Quick way, the database has that position and any possible derivate one
alpha = len(board_move)
beta_v = 1
pesos = []
for i in range(alpha):
r = beta.rvs(alpha, beta_v, size=1)
beta_v += difficulty
pesos.append((r[0]**7)*100)
pesos = sorted(pesos, reverse=True)
cum_pesos = np.cumsum(pesos)
choice = random.choices(board_move, cum_weights=cum_pesos, k=1)[0]
else:
choice = self.temp_backup()
except:
choice = self.temp_backup()
self.make_move(choice)
print(f'{choice} was moved.\n')
def blend(x, context, weights, respect_side=True, ab=0.8):
if len(x) < 2:
return x, context, weights
# the leftover data point if exists is dismissed (it will come up in future epochs, batches are shuffled)
if x.shape[0] % 2 > 0:
x = x[:-1]
context = context[:-1]
weights = weights[:-1]
if respect_side:
side_idx = x[:, 0].sort()[1]
x = x[side_idx]
context = context[side_idx]
weights = weights[side_idx]
b = torch.tensor(beta.rvs(ab, ab, size=x.shape[0] // 2),
device='cuda',
dtype=torch.float32).reshape(-1, 1)
# blending pairs
blended_x = b * x[::2] + (1 - b) * x[1::2]
blended_c = b * context[::2] + (1 - b) * context[1::2]
blended_w = b * weights[::2] + (1 - b) * weights[1::2]
# the side of the blended data points is collapsed to the closest value
blended_c[:, 0] = torch.where(b > 0.5, context[::2, 0].reshape(-1, 1),
context[1::2, 0].reshape(-1, 1)).squeeze()
return blended_x, blended_c, blended_w
def _test_mu_y_and_cov_y(self):
# NOTE: disable `skip_nullspace` for this test
alpha = 40.0
beta = 20.0
ln_sigma_mu = np.log(0.1)
ln_sigma_sigma = 0.0
ln_amp_mu = np.log(0.2)
ln_amp_sigma = 0.0
self.set_hyperparams(alpha, beta, ln_sigma_mu, ln_sigma_sigma,
ln_amp_mu, ln_amp_sigma)
x = -np.exp(ln_amp_mu)
amp = x / (x - 1)
sigma = np.exp(ln_sigma_mu)
nsamples = 100000
np.random.seed(1)
y = np.zeros((nsamples, (self.ydeg + 1)**2))
for n in range(nsamples):
lat = np.arccos(Beta.rvs(alpha, beta)) * 180.0 / np.pi
lat *= 2.0 * (int(np.random.random() > 0.5) - 0.5)
lon = 360.0 * np.random.random()
y[n] = self.y([amp], [sigma], [lat], [lon])
mu_y_num = np.mean(y, axis=0)
cov_y_num = np.cov(y.T)
# Compare
assert np.allclose(mu_y_num, self.mu_y, atol=1e-3)
assert np.allclose(cov_y_num, self.cov_y, atol=1e-3)
def sample_one(self):
xs = []
for a, b in zip(self.alphas, self.betas):
xs.append(beta.rvs(a, b, size=1)[0])
this_choice = argmax(xs)
logger.debug("a,b,[x_0,x_1,...], this_choice:", a, b, xs, this_choice)
return this_choice
def sample(self, X, n_samples=10):
n_items, _ = X.shape
betas = self.get_betas(X)
samples = beta.rvs(a=betas[:, 1],
b=betas[:, 0],
size=(n_samples, n_items))
return samples
def evaluate(self, node_list):
samples = [
beta.rvs(a=node.win_count + 1,
b=node.visit_count - node.win_count + 1,
size=1)[0] for node in node_list
]
return node_list[np.argmax(samples)]
def generate_data(self, N):
"""
Generates labels associated with the features
:param N: number of 1-D feature vector needed
:return: N x 1 numpy array with feature vectors, N x 1 numpy array with labels
"""
# Generate features
features = np.array(beta.rvs(a=self.alpha, b=self.beta, size=N))
features.resize((N, 1))
# Generate probabilities
probability = self.label_function(features)
# Generate noise to add to the probabilities
random_noise = np.random.normal(loc=0, scale=self.noise, size=features.shape[0])
random_noise.resize((features.shape[0], 1))
probability += random_noise
probability[probability < 0] = 0
probability[probability > 1] = 1
# Generate labels with a bernouilli
labels = np.array([np.random.binomial(n=1, p=probability[n:n+1]) for n in range(N)])
labels.resize((N, 1))
return features, labels
def simulate_stupidDPM(iter_num, M):
# Generate mixture sample
N = 1000
mu = [0.0, 10.0, 3.0]
components = np.random.choice(range(3), size = N, replace = True, p = [0.3, 0.5, 0.2])
samples = [norm.rvs(size = 1, loc = mu[components[i]], scale = 1)[0] for i in range(N)]
## Sample G from DP(M, G0)
v = beta.rvs(a = 1.0, b = M, size = N)
prob_vector = np.append(np.array(v[0]), v[1:] * np.cumprod(1.0 - v[:-1]))
thetas = norm.rvs(size = N, loc = 1.0, scale = 1.0)
### Initialize thetas
thetas = np.random.choice(thetas, size = N, replace = True, p = prob_vector)
### Start MCMC chain
for i in xrange(iter_num):
for j in xrange(N):
theta_temp = np.append(thetas[:j], thetas[j+1:])
p = np.append(norm.pdf(samples[j], loc = theta_temp, scale = 1.0), M * norm.pdf(samples[j], loc = 1.0, scale = np.sqrt(2.0)))
p = p / sum(p)
temp = np.random.choice(np.append(theta_temp, N), size = 1, replace = True, p = p)
if (temp == N):
thetas[j] = norm.rvs(size = 1, loc = 0.5 * (samples[j] + 1), scale = np.sqrt(0.5))
else:
thetas[j] = temp
print(thetas)
return {"thetas": thetas, "y": samples}
def get_iops(iops, duration):
iops_vals = [-1]
if iops['randvar'] == 'normal':
assert ('mean' in iops.keys() and 'stddev' in iops.keys()), \
"Error: invalid parameters for normal distribution!"
while min(iops_vals) < 0:
iops_vals = norm.rvs(size=duration, loc=iops['mean'], scale=iops['stddev']).astype(int).tolist()
elif iops['randvar'] == 'uniform':
assert ('min' in iops.keys() and 'max' in iops.keys()), \
"Error: invalid parameters for uniform distribution!"
while min(iops_vals) < 0:
iops_vals = uniform.rvs(size=duration, loc=iops['min'],
scale=iops['max']).astype(int).tolist()
elif iops['randvar'] == 'poisson':
assert ('mu' in iops.keys()), \
"Error: invalid parameters for poisson distribution!"
while min(iops_vals) < 0:
iops_vals = poisson.rvs(size=duration, mu=iops['mu']).astype(int).tolist()
elif iops['randvar'] == 'beta':
assert ('alpha' in iops.keys() and 'beta' in iops.keys()
and 'shift' in iops.keys() and 'scale' in iops.keys()), \
"Error: invalid parameters for beta distribution!"
while min(iops_vals) < 0:
iops_vals = beta.rvs(size=duration, a=iops['alpha'], b=iops['beta'],
loc=iops['shift'], scale=iops['scale']).astype(int).tolist()
else:
print("Error: invalid random distribution!")
exit(0)
print('Min IOPS: ' + str(min(iops_vals)))
print('Max IOPS: ' + str(max(iops_vals)))
return iops_vals
def thompson_sample(self, pred_ctr, k, max_pos, weight_type='propensity'):
"""
:param pred_ctr: list of predicted ctr
:param k: position to explore.(count from 1)
:param max_pos: explore candidate's maximum position.(count from 1)
:param weight_type: 'propensity' or 'multinomial'. otherwise assign unit weight.
:return: index of item to explore (index count from 0);
index of bucket/arm (count from 0);
the chosen arm's weight (propensity weight or multinomial weight.)
"""
assert 10 >= max_pos > k > 1
active_arms = []
for i, score in enumerate(pred_ctr[k - 1:max_pos]):
assert 0 <= score <= 1
temp = dict()
temp['exp_idx'] = i + k - 1
bucket_idx = int(score * self.bucket_size) % self.bucket_size
temp['bucket_idx'] = bucket_idx
temp['ts_score'] = beta.rvs(self.buckets[bucket_idx]['a'],
self.buckets[bucket_idx]['b'])
active_arms.append(temp)
arm_chosen = max(active_arms, key=lambda x: x['ts_score'])
self.exp_times += 1
self.exp_times_each[arm_chosen['bucket_idx']] += 1
if weight_type == 'propensity':
weight = 1.0 * self.exp_times / self.exp_times_each[
arm_chosen['bucket_idx']]
elif weight_type == 'multinomial':
weight = 1.0 * sum(
pred_ctr[k - 1:max_pos]) / pred_ctr[arm_chosen['exp_idx']]
else:
weight = 1
return arm_chosen['exp_idx'], arm_chosen['bucket_idx'], weight
def bandit(self, stop_alpha=0.05, stop_value=0.95, iterat=1000):
"""
Run bandit
Stop criterion:
1st Use Bayes' theorem to compute the probability that variation beats others, if 95% sure that a variation beats the others then "a winner has been found"
2nd Potential value remaining in the experiment - the "value remaining" is the amount of increased conversion rate we could get by switching away from the current champion
"""
simul_m = np.zeros((10000,self.n_arm))
stop_first = np.zeros((10000,1))
while ((iterat!=None) and (iterat>=self.k) ):
self.choose_arm()
for i in range(10000):
simul_m[i] = beta.rvs(1 + self.reward, 1 + 1*self.count - self.reward)
stop_first[i] = np.argmax(simul_m[i])
unique, counts = np.unique(stop_first, return_counts=True)
arm_prob = np.array((unique, counts/10000.0),dtype='float64').T
opt_arm = int(arm_prob[np.argmax(arm_prob[:,1], axis=0),0])
stop_second = np.percentile((np.max(simul_m,axis=1) - simul_m[:,opt_arm])/ simul_m[:,opt_arm], stop_value*100)
if np.max(arm_prob[:,1])>=(1-stop_alpha):
opt_arm = arm_prob[np.argmax(arm_prob[:,1], axis=0),0]
print('The winner has been found! The arm number {} has been found as optimal at the {} confidence level after {} page views.'.format(opt_arm,stop_alpha, self.k))
break
elif arm_prob[np.argmax(arm_prob[:,1], axis=0),1]*0.01 >=stop_second:
print('The winner has been found! The arm number {} has been found as optimal, as with {}% probability, the value remaining in the experiment is less than 1% possible improvement{}.'.format(opt_arm,stop_value*100,self.k))
break
elif iterat==self.k:
print('After {} iterations, the winning arm is number {}.'.format(iterat, opt_arm))
def update_current_sample(n_clicks):
# first sample from current posterior beta distribution
p = beta.rvs(a, b, size=1)[0]
print("Sample: " + str(p))
return json.dumps({'sample': p})
def simulation(n_seeds, n_targets, n_signals):
mixing_matrix = np.random.rand(n_seeds, n_signals)
mixing_matrix = mixing_matrix / np.sum(mixing_matrix**2, axis=0)
signals = np.zeros((n_signals, n_targets))
#beta parameters from fit to real aptx data
a = np.absolute(normal(0.25, 0.1, n_signals))
b = np.absolute(normal(640, 550, n_signals))
scale = np.absolute(normal(0.32, 0.26, n_signals))
#generate signals with beta distributions
for i in range(n_signals):
signals[i, :] = beta.rvs(a[i],
b[i],
loc=0,
scale=scale[i],
size=n_targets)
#rescale
signals = np.exp(signals) - 1
#normalise
signals = signals / (np.amax(signals, axis=1, keepdims=True) + 0.001)
return signals, mixing_matrix
def sample_lam(alpha, reformulate=False):
""" Sample a lambda from symmetric beta distribution with given alpha
:param alpha: Alpha value for beta distribution
:param reformulate: If True, uses the reformulation of [1].
"""
if reformulate:
lam = beta.rvs(
alpha + 1, alpha
) # rvs(arg1,arg2,loc=期望, scale=标准差, size=生成随机数的个数) 从分布中生成指定个数的随机数
else:
lam = beta.rvs(
alpha, alpha
) # rvs(arg1,arg2,loc=期望, scale=标准差, size=生成随机数的个数) 从分布中生成指定个数的随机数
return lam
def Prior_MCsim(self):
print('Generate ' + str(self.size) + " samples for " + self.name + ' distribution')
if self.name == 'Beta':
return beta.rvs(a = self.par[0], b = self.par[1], size = self.size, random_state = self.random_state)
else :
print("Not Available")
return []
def switching_binomial_motion(N_trials, N_blocks, tau, seed, Jeffreys=True, N_layer=3):
"""
A 3-layered model for generating samples.
about Jeffrey's prior: see https://en.wikipedia.org/wiki/Jeffreys_prior
"""
if Jeffreys:
from scipy.stats import beta
np.random.seed(seed)
trials = np.arange(N_trials)
p = np.random.rand(N_trials, N_blocks, N_layer)
for trial in trials:
# drawing all switches
p[trial, :, 2] = np.random.rand(1, N_blocks) < 1./tau
if Jeffreys:
p_random = beta.rvs(a=.5, b=.5, size=N_blocks)
else:
p_random = np.random.rand(1, N_blocks)
# drawing all probability biases
p[trial, :, 1] = (1 - p[trial, :, 2])*p[trial-1, :, 1] + p[trial, :, 2] * p_random
# drawing all samples
p[trial, :, 0] = p[trial, :, 1] > np.random.rand(1, N_blocks)
return (trials, p)
def iterate(number_of_ones, alpha):
ones = [] #count of x in every run
runs = args.runs
learning = args.learning
#expt=args.exponent
for r in range(runs):
if learning == "sample":
language = beta.rvs(alpha + number_of_ones,
alpha + (10 - number_of_ones)) # sampling
elif learning == "max":
language = (alpha + number_of_ones - 1) / (alpha * 2 + 10 - 2
) # maximising
elif learning == "avg":
language = (alpha + number_of_ones) / (alpha * 2 + 10) # averaging
data = [produce(language) for _ in range(10)] #one list of 01s
#print data
count_of_ones = float(data.count(1))
ones.append(count_of_ones)
#if r < 10:
#print number_of_ones
#print "list of ones: ",ones[1:10]
#dictionary with x_possible_values:freqs(x), ordered by n_of_x
d = {}
for c in ones:
count = ones.count(c)
d[c] = count
#print "dictionary: ",d.items()[1:10]
#get probabilities of proportion_of_ones as list of tuples (n,prob(n))
prob = [(n, float(freq) / len(ones)) for n, freq in d.items()]
return prob
def property_beta_distribution(n_props, beta_prop_1, beta_prop_2): # Samples from beta probability distribution # Output: matrix of probabilities (size n_props) property_probs = beta.rvs(beta_prop_1, beta_prop_2, size = n_props) probabilities = np.array(property_probs) probs_shape = probabilities.reshape(1, n_props) return probs_shape
def gene_beta_distribution(n_genes, beta_gene_1, beta_gene_2): # Samples from beta probability distribution # Output: matrix of probabilities (size n_genes) gene_probs = beta.rvs(beta_gene_1, beta_gene_2, size = n_genes) genes = np.array(gene_probs) properties_genes = genes.reshape(n_genes, 1) return properties_genes
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 ep2_rvs(mu, sigma, alpha, size=1):
u = uniform.rvs(loc=0, scale=1, size=size)
b = beta.rvs(1. / alpha, 1 - 1. / alpha, size=size)
r = np.sign(uniform.rvs(loc=0, scale=1, size=size) - .5)
z = r * (-alpha * b * np.log(u))**(1. / alpha)
return z
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 __init__(self, n_arms, random_seed=None):
if random_seed is not None:
np.random.seed(random_seed)
self.arm_probs = {}
self.arm_features = []
temp_val_list = []
scaled_vals = []
self.arm_probs = beta.rvs(1,1,size=n_arms)
def sample_an_arm():
max_sample = float('-inf')
best_arm = None
for arm in range(0, arm_num):
r = beta.rvs(prior[arm][0], prior[arm][1])
# print r
if r > max_sample:
max_sample = r
best_arm = arm
return best_arm
def get_pi(cl_ind, g_values, data):
cl = [cl_ind*2, cl_ind*2+1]
count_p, count_m = 0, 0
for obj in cl:
for g in g_values[obj][1]:
if g == 1:
count_p += 1
else:
count_m += 1
pi_new = beta.rvs(count_p + gamma1, count_m + gamma2, size=1)[0]
return pi_new
def _make_data(self):
self.Z = []
self.counts = []
self.Z.append(1)
self.counts.append(1)
for n in np.arange(1,self.n_peaks):
# List concatanation
temp = np.array(self.counts + [self.hyper_pars.conc_par])
temp = temp/temp.sum()
prob = temp.cumsum()
pos = np.nonzero(np.random.rand()<prob)[0][0]
self.Z.append(pos)
if pos >= len(self.counts):
self.counts.append(1)
else:
self.counts[pos] += 1
self.Z = np.sort(self.Z)
self.K = np.max(self.Z)
self.intensities = []
for n in np.arange(self.n_peaks):
self.intensities.append(np.random.rand())
# self.corr_mat = np.zeros((self.n_peaks,self.n_peaks))
self.corr_mat = lil_matrix((self.n_peaks,self.n_peaks),dtype=np.float64)
for n in np.arange(self.n_peaks-1):
this_cluster = self.Z[n]
for m in np.arange(n+1,self.n_peaks):
if self.Z[m] == this_cluster:
if np.random.rand() < self.hyper_pars.in_prob:
this_val = beta.rvs(self.hyper_pars.in_alpha,self.hyper_pars.in_beta)
else:
this_val = 0
else:
if np.random.rand() < self.hyper_pars.out_prob:
this_val = beta.rvs(self.hyper_pars.out_alpha,self.hyper_pars.out_beta)
else:
this_val = 0
if this_val >0:
self.corr_mat[n,m] = this_val
self.corr_mat[m,n] = this_val
def draw_from_beta_distributions(alphas, betas, possible_pages):
max_id = 0
max_=0
for i in range(0, (alphas.size-1)):
beta_outcome = float(beta.rvs(alphas[i], betas[i])*float(possible_pages[i]['price']/50))
if beta_outcome > max_:
max_ = beta_outcome
max_id = i
return max_id
def run_model(self, *args, **kwargs):
groups = kwargs.get('groups', self.groups)
samples = kwargs.get('samples', self.samples)
df = kwargs.get('df', self.df)
for group in groups:
group_data = df[df[self.groupcol] == group]
total = group_data[self.totalcol]
successes = group_data[self.successcol]
mc_data = beta.rvs(
successes + self.alpha_, total - successes + self.beta_, size=samples)
self.distributions[group] = mc_data
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 sample(self,m):
"""
Samples m samples from the current Kumaraswamy distribution.
:param m: Number of samples to draw.
:type m: int.
:rtype: natter.DataModule.Data
:returns: A Data object containing the samples
"""
return Data(self.param['B']*beta.rvs(1,self.param['b'],size=m)**(1/self.param['a']),'%i samples from %s' % (m,self.name))
def __call__(self, nvot, ncand, vType=DimVoter):
"""Tests? Making statistical tests that would pass reliably is
a huge hassle. Sorry, maybe later.
"""
vType.resetClusters()
e = self.builtElectorate()
e.dcs = [] #number of dimensions in each dc
e.dimWeights = [] #raw importance of each dimension, regardless of dc
clusterWeight = 1
while clusterWeight > self.dccut:
dimweight = clusterWeight
dimnum = 0
while dimweight > self.wccut:
e.dimWeights.append(dimweight)
dimnum += 1
dimweight *= beta.rvs(*self.wcdecay)
e.dcs.append(dimnum)
clusterWeight *= beta.rvs(*self.dcdecay)
e.numClusters = len(e.dcs)
e.numSubclusters = [0] * e.numClusters
e.chooseClusters(nvot + ncand, self.wcalpha, lambda:beta.rvs(*self.vccaring))
return self.makeElectorate(e, nvot, ncand, vType)
def plot2(_list, chr_brps, centromere_brps, line_names=None):
if not line_names:
line_names = range(1, _list.shape[0]+1)
inflated_table = np.vstack([inflate_tags(x[0, :], 25) for x in np.split(_list, _list.shape[0])])
gs = gridspec.GridSpec(4, 4)
ax1 = plt.subplot(gs[:-1, :])
plt.imshow(inflated_table, interpolation='nearest', cmap='coolwarm')
show_breakpoints([0] + chr_brps + [_list.shape[1]], 'k')
show_breakpoints(list(set(centromere_brps) - set(chr_brps)), 'g')
ax2 = plt.subplot(gs[-1, :], sharex=ax1)
red_run = np.nanmean((_list > 0).astype(np.float), 0)
blue_run = np.nanmean((_list < 0).astype(np.float), 0)
stack = np.hstack((blue_run, red_run))
mean = np.mean(stack)
std = np.std(stack)
_alpha = ((1 - mean)/std**2 - 1/mean)*mean**2
_beta = _alpha*(1/mean-1)
r = beta.rvs(_alpha, _beta, size=1000)
_min, _max = beta.interval(0.95, _alpha, _beta)
plt.plot(blue_run, 'b')
plt.plot(red_run, 'r')
plt.axhline(y=_min, color='g')
plt.axhline(y=_max, color='g')
show_breakpoints([0] + chr_brps + [_list.shape[1]], 'k')
show_breakpoints(list(set(centromere_brps) - set(chr_brps)), 'g')
chr_arm_locations, chr_arm_names = sf.align_chromosome_edges(chr_brps, centromere_brps)
ax1.set_xticks(chr_arm_locations)
ax1.set_xticklabels(chr_arm_names, rotation='vertical')
ax1.set_yticks(range(0, _list.shape[0]*25+1, 25))
ax1.set_yticklabels(line_names)
ax2.set_xticks(chr_arm_locations)
ax2.set_xticklabels(chr_arm_names, rotation='vertical')
plt.show()
smooth_histogram(r, 'b')
smooth_histogram(stack)
plt.axvline(x=_max, color='g')
plt.axvline(x=_min, color='g')
plt.show()
def get_a(counts, s_ind):
count_p, count_m = 0, 0
for obj in counts.keys():
sources = counts[obj][0]
if s_ind not in sources:
continue
c_ind = sources.index(s_ind)
c = counts[obj][1][c_ind]
if c == 1:
count_p += 1
else:
count_m += 1
a_new = beta.rvs(count_p + alpha1, count_m + alpha2, size=1)[0]
return a_new
def guess_preferences(self):
"""
Input: no inputs
Output: no outputs
Notes: this function will take the updated score for each metric, compute a
beta distribution defined by the win/loss scores, sample from each distribution
and return the metric that corresponds to the greatest probability. The winning
metric is added to recommendation_history as the best guess of user preference.
"""
user_preference = None
max_prob = 0
for metric in self.metrics:
self.params[metric] = (self.scores[metric] + 1, self.pairs_served - self.scores[metric] + 1)
prob = beta.rvs(self.params[metric][0] + 1, self.params[metric][1] + 1) # sample form the dist for each metric
if prob > max_prob:
max_prob = prob
user_preference = metric
self.recommendation_history[self.pairs_served]['estimated_user_preference'] = user_preference
def beta_dist(N, Nx, a, b):
N = int(N)
xis = beta.rvs(a, b, size=N)
xmin = 0.0
xmax = 1.0
# Create a grid of points and do initial evaluate of Qtrue
xgrid = sp.linspace(xmin, xmax, Nx)
Qtrue = beta.pdf(xgrid, a, b)
# Remove indices where Qtrue blows up
indices = (Qtrue > -sp.inf)*(Qtrue < sp.inf)
Qtrue = Qtrue[indices]
xgrid = xgrid[indices]
# Return
return [xis, xgrid, Qtrue]
def plot_results(B,pcF,a,b,loc,scale,numsamp,sampsize,curcolor,lstyle,ax):
Bfit,pcFfit,RMSE,a,b,loc,scale=betadist_leastsquare_fitting(B,pcF,a,b, loc, scale)
beta_mode=scale*(a-1.)/(a+b-2.)
ax[0].plot(Bfit,pcFfit,ls=lstyle,color=curcolor, label=Lith_list[j])
ax[0].plot(B,pcF,'x',c=curcolor)
ax[0].legend(fontsize='x-small', loc='best')
if Lith_list[j]=='UM':
ax[0].grid(b=None, which='major', axis='x')
#generate random variables from beta distribution
betarvs=beta.rvs(a,b,loc=loc,scale=scale,size=numsamp*100)
ax[1].hist(betarvs, bins=np.linspace(0,5,1+int(5/0.05)),normed=True, histtype='step',lw=0.5,color=curcolor)
ax[1].plot(Bfit, beta.pdf(Bfit,a,b,loc=loc,scale=scale),ls=lstyle,color=curcolor,lw=2)
if Lith_list[j]=='UM':
ax[1].grid(b=None, which='major', axis='x')
samp_modes=np.empty(numsamp)
for i in range(numsamp):
indx=np.arange(len(betarvs))
np.random.shuffle(indx)
indx=indx[:sampsize]
#computing and appending sample mode
kde,maxima=kde_minmode(betarvs[indx],np.linspace(0,5,1+int(5/0.001)),2,0.1)
samp_modes[i]=maxima[np.argmax(kde(maxima))]
#ax[2].plot(np.linspace(0,5,1+int(5/0.001)),kde(np.linspace(0,5,1+int(5/0.001))),'-',c='0.6')
kde,maxima=kde_minmode(samp_modes,np.linspace(0,5,1+int(5/0.001)),2,0.1)
#ax[2].plot(np.linspace(0,5,1+int(5/0.001)),kde(np.linspace(0,5,1+int(5/0.001))),'-',c=curcolor)
ksnorm_pval=kstest(samp_modes,'norm',args=(np.mean(samp_modes),np.std(samp_modes)))[1]
if round(ksnorm_pval,3)>0.05:
ax[2].hist(samp_modes, bins=np.linspace(0,5,1+int(5/0.05)),normed=True, histtype='step',lw=0.5,color=curcolor)
ax[2].plot(np.linspace(0,5,1+int(5/0.001)),norm.pdf(np.linspace(0,5,1+int(5/0.001)),loc=np.mean(samp_modes),scale=np.std(samp_modes)),
'-',c=curcolor, lw=2)
if Lith_list[j]=='UM':
ax[2].grid(b=None, which='major', axis='x')
return a,b, round(beta_mode,3),round(ksnorm_pval,3), samp_modes
def get_a(data, s, counts):
count_p = 0
count_m = 0
obj_index_list = data.keys()
for obj_index in obj_index_list:
obj_data = data[obj_index]
sources = obj_data[0]
if s not in sources:
continue
obj_counts = counts[obj_index][1]
s_index = sources.index(s)
c = obj_counts[s_index]
if c == 1:
count_p += 1
else:
count_m += 1
a_new = beta.rvs(count_p + alpha1, count_m + alpha2, size=1)[0]
return a_new
def sample_an_arm():
max_sample = float('-inf')
best_arm = None
mature_ones = []
for arm in range(0, arm_num):
if win[arm] >= 10 and prior[arm][0] < prior[arm][1]:
continue
if win[arm] >= 10 and prior[arm][0] > prior[arm][1]:
mature_ones.append(arm)
continue
r = beta.rvs(prior[arm][0], prior[arm][1])
# print r
if r > max_sample:
max_sample = r
best_arm = arm
if len(mature_ones) > 0:
return find_best_one(mature_ones)
if best_arm is not None:
return best_arm
else:
return find_best_one(range(bandit.arm_num()))
