def generate_data(self,n=1e4, k=2, ncomps=3, seed=1):
npr.seed(seed)
data1_concat = []
data2_concat = []
labels1_concat = []
labels2_concat = []
for j in xrange(ncomps):
mean = gen_mean[j]
sd = gen_sd[j]
corr = gen_corr[j]
cov = np.empty((k, k))
cov.fill(corr)
cov[np.diag_indices(k)] = 1
cov *= np.outer(sd, sd)
num1 = int(n * group_weights1[j])
num2 = int(n * group_weights2[j])
rvs1 = multivariate_normal(mean, cov, size=num1)
rvs2 = multivariate_normal(mean, cov, size=num2)
data1_concat.append(rvs1)
data2_concat.append(rvs2)
labels1_concat.append(np.repeat(j, num1))
labels2_concat.append(np.repeat(j, num2))
return ([np.concatenate(labels1_concat), np.concatenate(labels2_concat)],
[np.concatenate(data1_concat, axis=0),
np.concatenate(data2_concat, axis=0)])
def genDataset(N, separable = True):
n = N//2
if separable:
mu1 = np.array([0, 2])
mu2 = np.array([2, 0])
sigma = np.array([[0.8, 0.6], [0.6, 0.8]])
X1 = rn.multivariate_normal(mu1, sigma, N)
Y1 = np.ones(len(X1))
X2 = rn.multivariate_normal(mu2, sigma, N)
Y2 = np.ones(len(X2)) * -1
X = np.vstack((X1, X2))
Y = np.hstack((Y1, Y2))
else:
X = np.random.randn(300, 2)
Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
mu1 = [-1, 2]
mu2 = [1, -1]
mu3 = [4, -4]
mu4 = [-4, 4]
sigma = [[1.0,0.8], [0.8, 1.0]]
X1 = rn.multivariate_normal(mu1, sigma, n)
X1 = np.vstack((X1, np.random.multivariate_normal(mu3, sigma, n)))
Y1 = np.ones(len(X1))
X2 = rn.multivariate_normal(mu2, sigma, n)
X2 = np.vstack((X2, np.random.multivariate_normal(mu4, sigma, n)))
Y2 = np.ones(len(X2)) * -1
X = np.vstack((X1, X2))
Y = np.hstack((Y1, Y2))
return X, Y
def step(self, y, predict_P=False, index=1):
X = self.x_prior
states = X[:2, :]
params = X[2:, :]
s_std = std(X[index].A1)
tmp_ws = as_array([norm.pdf(y, x[0, index], s_std) for x in states.T])
n_weights = self.weights * tmp_ws
sum_weights = n_weights.sum()
if sum_weights != 0:
n_weights /= sum_weights
neff = 1.0 / (n_weights ** 2).sum()
if sum_weights == 0 or neff < self.num_part/2.0:
idx = choice(range(X.shape[1]), X.shape[1], p=self.weights)
self.weights = tile(as_array(1.0 / self.num_part), self.num_part)
self.x_post = X[:, idx]
else:
self.x_post = X
self.weights = n_weights
p_mean = average(params, axis=1, weights=self.weights).A1
p_cov = cov(params, aweights=self.weights)
self.x_post[2:, :] = multivariate_normal(p_mean, p_cov, X.shape[1]).T
for i, x in enumerate(self.x_post[2:, :].T):
if x.any() < 0:
while True:
new = multivariate_normal(p_mean, p_cov, 1).T
if new.all() > 0 and new[0, 1] > new[0, 2]:
self.x_post[2:, i] = new
break
def rdm_pnt(nbr_cluster):
from numpy.random import uniform
from numpy.random import multivariate_normal
from numpy import savetxt
import sys
x = uniform(50, 950)
y = uniform(50, 950)
z = uniform(-500, 500) + 20.0
cov = [[uniform(-5, 5), z], [z, uniform(-5, 5)]]
s = multivariate_normal([x, y], cov, nbr_cluster[1])
xo = s[:, 0]
yo = s[:, 1]
print s
sys.stdout.flush()
for i in range(nbr_cluster[0] - 1):
x = uniform(50, 950)
y = uniform(50, 950)
z = uniform(-500, 500) + 20.0
cov = [[uniform(-5, 5), z], [z, uniform(-5, 5)]]
s = multivariate_normal([x, y], cov, nbr_cluster[1])
xo.extend(s[:, 0])
yo.extend(s[:, 1])
print xo
sys.stdout.flush()
savetxt("data.txt", s)
return 1
def sample(self, T, s_init=None,x_init=None,y_init=None):
"""
Inputs:
T: time to run simulation
Outputs:
xs: Hidden continuous states
Ss: Hidden switch states
"""
x_dim, y_dim, = self.x_dim, self.y_dim
# Allocate Memory
xs = zeros((T, x_dim))
Ss = zeros(T)
# Compute Invariant
_, vl = linalg.eig(self.Z, left=True, right=False)
pi = vl[:,0]
# Sample Start conditions
sample = multinomial(1, pi, size=1)
if s_init == None:
Ss[0] = nonzero(sample)[0][0]
else:
Ss[0] = s_init
if x_init == None:
xs[0] = multivariate_normal(self.mus[Ss[0]], self.Sigmas[Ss[0]])
else:
xs[0] = x_init
# Perform time updates
for t in range(0,T-1):
s = Ss[t]
A = self.As[s]
b = self.bs[s]
Q = self.Qs[s]
xs[t+1] = multivariate_normal(dot(A,xs[t]) + b, Q)
sample = multinomial(1,self.Z[s],size=1)[0]
Ss[t+1] = nonzero(sample)[0][0]
return (xs, Ss)
def test():
from .walk import walk
from numpy.random import multivariate_normal, seed
from numpy import vstack, ones, eye
seed(2) # Remove uncertainty on tests
# Set a number of good and bad chains
Ngood,Nbad = 25,2
# Make chains mean-reverting chains with widely separated values for
# bad and good; put bad chains first.
chains = walk(1000, mu=[1]*Nbad+[5]*Ngood, sigma=0.45, alpha=0.1)
# Check IQR and Grubbs
assert (identify_outliers('IQR',chains,None) == arange(Nbad)).all()
assert (identify_outliers('Grubbs',chains,None) == arange(Nbad)).all()
# Put points for 'bad' chains at [-1,...,-1] and 'good' chains at [1,...,1]
x = vstack( (multivariate_normal(-ones(4),.1*eye(4),size=Nbad),
multivariate_normal(ones(4),.1*eye(4),size=Ngood)) )
assert identify_outliers('Mahal',chains,x)[0] in range(Nbad)
# Put points for _all_ chains at [1,...,1] and check that mahal return []
xsame = multivariate_normal(ones(4),.2*eye(4),size=Ngood+Nbad)
assert len(identify_outliers('Mahal',chains,xsame)) == 0
# Check again with large variance
x = vstack( (multivariate_normal(-3*ones(4),eye(4),size=Nbad),
multivariate_normal(ones(4),10*eye(4),size=Ngood)) )
assert len(identify_outliers('Mahal',chains,x)) == 0
# =====================================================================
# Test replacement
# Construct a state object
from numpy.linalg import norm
from .state import MCMCDraw
Ngen, Npop = chains.shape
Npop, Nvar = x.shape
state = MCMCDraw(Ngen=Ngen, Nthin=Ngen, Nupdate=0,
Nvar=Nvar, Npop=Npop, Ncr=0, thinning=0)
# Fill it with chains
for i in range(Ngen):
state._generation(new_draws=Npop, x=x, logp=chains[i], accept=Npop)
# Make a copy of the current state so we can check it was updated
nx, nlogp = x+0,chains[-1]+0
# Remove outliers
state.remove_outliers(nx, nlogp, test='IQR', portion=0.5)
# Check that the outliers were removed
outliers = state.outliers()
assert outliers.shape[0] == Nbad
for i in range(Nbad):
assert nlogp[outliers[i,1]] == chains[-1][outliers[i,2]]
assert norm(nx[outliers[i,1],:] - x[outliers[i,2],:]) == 0
def prepare_dataset(variance):
cov1 = np.array([[variance,0],[0,variance]])
cov2 = np.array([[variance,0],[0,variance]])
df1 = DataFrame(multivariate_normal(Mu1,cov1,N1),columns=['x','y'])
df1['type'] = 1
df2 = DataFrame(multivariate_normal(Mu2,cov2,N2),columns=['x','y'])
df2['type'] = -1
df = pd.concat([df1,df2],ignore_index=True)
df = df.reindex(np.random.permutation(df.index)).reset_index(drop=True)
return df
def sample():
from numpy.random import rand, multivariate_normal
data_a = multivariate_normal(rand(1) * 20 - 10, np.eye(1) * (rand()), 250)
data_b = multivariate_normal(rand(1) * 20 - 10, np.eye(1) * (rand()), 250)
data_c = multivariate_normal(rand(1) * 20 - 10, np.eye(1) * (rand()), 250)
data_d = multivariate_normal(rand(1) * 20 - 10, np.eye(1) * (rand()), 250)
X = np.r_[data_a, data_b, data_c, data_d][:, 0]
c_cf = change_finder(term=70, window=7, order=(2, 2, 0))
result = c_cf.main(X)
return result
def generate_data():
sz = 100
mu1 = randint(1,1000,size=sz)
mu2 = randint(1000,2000,size=sz)
mu3 = randint(3000,4000,size=sz)
cov = np.eye(sz)
x1 = multivariate_normal(mu1, cov, 100)
x2 = multivariate_normal(mu2, cov, 20)
x3 = multivariate_normal(mu3, cov, 50)
return np.vstack((x1,x2,x3))
def move(self, s, a):
s = np.copy(s)
if a == "up":
s = s + self.move_up + npr.multivariate_normal(np.array([0., 0]), np.diag([0.25, 0.125]))
elif a == "down":
s = s + self.move_down + npr.multivariate_normal(np.array([0., 0]), np.diag([0.25, 0.125]))
elif a == "left":
s = s + self.move_left + npr.multivariate_normal(np.array([0., 0]), np.diag([0.125, 0.25]))
elif a == "right":
s = s + self.move_right + npr.multivariate_normal(np.array([0., 0]), np.diag([0.125, 0.25]))
else:
pass
# print s
return s
def prepare_dataset(variance):
n1 = 80
n2 = 200
mu1 = [9,9]
mu2 = [-3,-3]
cov1 = np.array([[variance,0],[0,variance]])
cov2 = np.array([[variance,0],[0,variance]])
df1 = DataFrame(multivariate_normal(mu1,cov1,n1),columns=['x','y'])
df1['type'] = 1
df2 = DataFrame(multivariate_normal(mu2,cov2,n2),columns=['x','y'])
df2['type'] = 0
df = pd.concat([df1,df2],ignore_index=True)
df = df.reindex(np.random.permutation(df.index)).reset_index()
return df[['x','y','type']]
def GenerateGaussians(N, Ntr):
# Generate the data.
mu0, mu1 = np.array([1.0, 1.0]), np.array([-1.0, -1.0])
sg = np.array([[1.0, -0.75], [-0.75, 1.0]])
X0 = rnd.multivariate_normal(mu0, sg, size=(N)).astype(floatX)
X1 = rnd.multivariate_normal(mu1, sg, size=(N)).astype(floatX)
Y0 = np.hstack((np.ones((N, 1)), np.zeros((N, 1)))).astype(floatX)
Y1 = np.hstack((np.zeros((N, 1)), np.ones((N, 1)))).astype(floatX)
# Permute the data and split to return.
idx = rnd.permutation(2*N)
X, Y = np.vstack((X0, X1))[idx], np.vstack((Y0, Y1))[idx]
return shared(X[:Ntr], 'Xtr'), shared(Y[:Ntr], 'Ytr'), \
shared(X[Ntr:], 'Xte'), shared(Y[Ntr:], 'Yte')
def sample_gp_posterior_mean(gp, coords_ptr, coords_size, bounds_ptr, \
sample_x_ptr, num_samples, check_constraint = None, exp_temp = 10, MH_rate = 0.09):
"""
Sample by using the Metropolis method to get policies in the re-sampling phase.
We treat the exponentiated (GP-mean*exp_temp) as unnormalized density.
The proposal distribution is normal distribution: N(Mu, MH_rate*I).
"""
bounds = [(bounds_ptr[2*i], bounds_ptr[2*i+1]) for i in xrange(coords_size)]
def check_violate_constraint(nx_pt, bounds, coords_size, check_constraint = None):
for i in xrange(coords_size):
if not (nx_pt[i] <= bounds[i][1] and nx_pt[i] >= bounds[i][0]):
return True
if not check_constraint is None:
return check_constraint(nx_pt, bounds, coords_size)
return False
coords = zeros(coords_size)
for i in xrange(coords_size):
coords[i] = coords_ptr[i]
import numpy.random as nr
cur_pt = coords
factor = MH_rate
cov = eye(coords_size)
for i in xrange(coords_size):
cov[i,i] = (bounds[i][1] - bounds[i][0])**2*factor;
for i in xrange(num_samples):
for j in xrange(coords_size):
sample_x_ptr[i*coords_size+j] = cur_pt[j]
nx_pt = nr.multivariate_normal(cur_pt, cov)
while check_violate_constraint(nx_pt, bounds, coords_size, check_constraint):
nx_pt = nr.multivariate_normal(cur_pt, cov)
mh_ratio = exp((- gp.posterior(cur_pt)[0] + gp.posterior(nx_pt)[0])*exp_temp)
r = nr.uniform()
if r < mh_ratio:
cur_pt = nx_pt
return 0
def setUp(self):
self.mu = array([0, 0])
self.sig = eye(2)
self.pnts = multivariate_normal(self.mu, self.sig, 1000)
self.k = 16
self.niter = 10
self.model = DPMixtureModel(self.k, self.niter, 0, 1)
def plot_monte_carlo_ukf():
def f(x,y):
return x+y, .1*x**2 + y*y
mean = (0, 0)
p = np.array([[32, 15], [15., 40.]])
# Compute linearized mean
mean_fx = f(*mean)
#generate random points
xs, ys = multivariate_normal(mean=mean, cov=p, size=3000).T
fxs, fys = f(xs, ys)
plt.subplot(121)
plt.gca().grid(b=False)
plt.scatter(xs, ys, marker='.', alpha=.2, color='k')
plt.xlim(-25, 25)
plt.ylim(-25, 25)
plt.subplot(122)
plt.gca().grid(b=False)
plt.scatter(fxs, fys, marker='.', alpha=0.2, color='k')
plt.ylim([-10, 200])
plt.xlim([-100, 100])
plt.show()
def mixnormrnd(pi, mu, sigma, k):
"""Generate random variables from mixture of Guassians"""
xs = []
for unused in range(k):
j = sum(random() > cumsum(pi))
xs.append(multivariate_normal(mu[j], sigma[j]))
return array(xs)
def simulate(self, ts_length=100):
"""
Simulate a time series of length ts_length, first drawing
x_0 ~ N(mu_0, Sigma_0)
Parameters
----------
ts_length : scalar(int), optional(default=100)
The length of the simulation
Returns
-------
x : array_like(float)
An n x ts_length array, where the t-th column is x_t
y : array_like(float)
A k x ts_length array, where the t-th column is y_t
"""
x0 = multivariate_normal(self.mu_0.flatten(), self.Sigma_0)
w = np.random.randn(self.m, ts_length-1)
v = self.C.dot(w) # Multiply each w_t by C to get v_t = C w_t
# == simulate time series == #
x = simulate_linear_model(self.A, x0, v, ts_length)
if self.H is not None:
v = np.random.randn(self.l, ts_length)
y = self.G.dot(x) + self.H.dot(v)
else:
y = self.G.dot(x)
return x, y
def simulate_mixed_logit(num_pers,predict_data,config): num=num_pers to_load=zeros(len(predict_data)) while num>0: exputils=zeros(len(predict_data)) for path_idx in range(len(predict_data)): vals=predict_data[path_idx] u=0 for i in range(len(config['fixed_coefficients'])): u=u+config['alpha'][i]*vals[config['fixed_coefficients'][i]] if config['use_random_coefficients']: beta=nr.multivariate_normal(config['latent_mu'],config['latent_sigma']) for i in range(len(beta)): beta[i]=config['random_transformations'][i](beta[i]) u=u+beta[i]*vals[config['random_coefficients'][i]] exputils[path_idx]=exp(u) if num>config['mixing_granularity'] and config['use_random_coefficients']: to_load=to_load+config['mixing_granularity']*exputils/sum(exputils) else: to_load=to_load+num*exputils/sum(exputils) if config['use_random_coefficients']: num=num-config['mixing_granularity'] else: num=0 return to_load
def sampleConditionalDistribution(self, indices, values):
#Calculate cumulative indices in the mean vector
counter = 0
cum_indices = []
for i in range(len(self.dims)):
if i in indices:
for j in range(counter,counter+self.dims[i]):
cum_indices.append(j)
counter += self.dims[i]
#Mask with newly calculated indices
state_mask = ones(self.state_dim,dtype=bool)
state_mask[cum_indices] = False
condition_mask = logical_not(state_mask)
s11 = self.covar[state_mask][:,state_mask]
s12 = self.covar[state_mask][:,condition_mask]
s21 = self.covar[condition_mask][:,state_mask]
s22 = self.covar[condition_mask][:,condition_mask]
m1 = zeros((sum(state_mask),1))
m2 = zeros((sum(condition_mask),1))
#Project conditioned values
value_projected = matrix([[]]).reshape((0,1))
for i in xrange(len(values)):
value_projected = matrix(concatenate((value_projected,self.mean[indices[i]].log(values[i]))))
#Calculate new mean
m_prime = m1 + s12*s22.getI()*(value_projected-m2)
s_prime = s11 - s12*s22.getI()*s21
sample = matrix(random.multivariate_normal(mean=m_prime.getT().tolist()[0], cov=s_prime)).getT()
#Pack in order to return
rest = list(set(range(len(self.dims)))-set(indices))
return packPoints(self.mean, [sample], rest, self.types, self.dims)[0]
def simulate(self, ts_length=100):
"""
Simulate a time series of length ts_length, first drawing
x_0 ~ N(mu_0, Sigma_0)
Parameters
----------
ts_length : scalar(int), optional(default=100)
The length of the simulation
Returns
-------
x : array_like(float)
An n x ts_length array, where the t-th column is x_t
y : array_like(float)
A k x ts_length array, where the t-th column is y_t
"""
x = np.empty((self.n, ts_length))
x[:, 0] = multivariate_normal(self.mu_0.flatten(), self.Sigma_0)
w = np.random.randn(self.m, ts_length - 1)
for t in range(ts_length - 1):
x[:, t + 1] = self.A.dot(x[:, t]) + self.C.dot(w[:, t])
y = self.G.dot(x)
return x, y
def _produceNewSample(self):
""" returns a new sample, its fitness and its densities """
chosenOne = drawIndex(self.alphas, True)
mu = self.mus[chosenOne]
if self.useAnticipatedMeanShift:
if len(self.allsamples) % 2 == 1 and len(self.allsamples) > 1:
if not(self.elitism and chosenOne == self.bestChosenCenter):
mu += self.meanShifts[chosenOne]
if self.diagonalOnly:
sample = normal(mu, self.sigmas[chosenOne])
else:
sample = multivariate_normal(mu, self.sigmas[chosenOne])
if self.sampleElitism and len(self.allsamples) > self.windowSize and len(self.allsamples) % self.windowSize == 0:
sample = self.bestEvaluable.copy()
fit = self._oneEvaluation(sample)
if ((not self.minimize and fit >= self.bestEvaluation)
or (self.minimize and fit <= self.bestEvaluation)
or len(self.allsamples) == 0):
# used to determine which center produced the current best
self.bestChosenCenter = chosenOne
self.bestSigma = self.sigmas[chosenOne].copy()
if self.minimize:
fit = -fit
self.allfitnesses.append(fit)
self.allsamples.append(sample)
return sample, fit
def sample_from_posterior_given_hypers_and_data(self, pred, n_samples=1, joint=True):
if joint:
predicted_mean, cov = self.predict(pred, full_cov=True) # This part depends on the data
return npr.multivariate_normal(predicted_mean, cov, size=n_samples).T.squeeze()
else:
predicted_mean, var = self.predict(pred, full_cov=False) # This part depends on the data
return np.squeeze(predicted_mean[:,None] + npr.randn(pred.shape[0], n_samples) * np.sqrt(var)[:,None])
def gibbs_sample(groups, votes, n_samples, n_burnin):
""" Performs Gibbs Sampling over groups.
Observations:
- Each Variable object has a list of samples. Once a new sample is
generated, the value used for the variable is the new sample.
- After sampling, the values of latent variables are changed to empiric
mean of samples.
Args:
groups: a dict of Group objects.
votes: list of votes, each one represented as a dictionary, which is the
training data.
n_samples: the number of samples to obtain.
n_burnin: number of initial samples to ignore.
Returns:
None. The samples are inserted into Variable objects.
"""
burn_count = 0
for _ in xrange(n_samples + n_burnin):
for g_name in ['alpha', 'beta', 'xi', 'u', 'v', 'gamma', 'lambda']:
group = groups[g_name]
if isinstance(group, EntityArrayGroup):
for variable in group.iter_variables():
mean, var = variable.get_cond_mean_and_var(groups, votes)
sample = multivariate_normal(mean.reshape(-1), var)
variable.add_sample(sample.reshape(sample.size, 1))
else:
for variable in group.iter_variables():
mean, var = variable.get_cond_mean_and_var(groups, votes)
sample = normal(mean, sqrt(var))
variable.add_sample(sample)
if burn_count < n_burnin:
burn_count += 1
def copula(num_samples, rho_mat, mu_mat, methods):
"""Copula procedure to generate an OTU table with corrs close to rho_mat.
Inputs:
num_samples - int, number of samples.
rho_mat - 2d arr, symmetric positive definite matrix which specifies the
correlation or covariation between the otu's in the table.
mu_mat - 1d arr w/ len(num_otus), mean of otu for multivariate random call.
methods - list of lists w/ len(num_otus), each list has a variable number
of elements. the first element in each list is the
scipy.stats.distributions function like lognorm or beta. this is the
function that we draw values from for the actual otu. the remaining entries
are the parameters for that function in order that the function requires
them.
"""
num_otus = len(mu_mat)
# draw from multivariate normal distribution with specified parameters.
# transpose so that it remains otuXsample matrix.
Z = multivariate_normal(mean=mu_mat, cov=rho_mat, size=num_samples).T
# using the inverse cdf of the normal distribution find where each sample
# value for each otu falls in the normal cdf.
U = norm.cdf(Z)
# make the otu table using the methods and cdf values. ppf_args[0] is the
# distribution function (eg. lognorm) whose ppf function we will use
# to transform the cdf vals into the new distribution. ppf_args[1:] is the
# params of the function like a, b, size, loc etc.
otu_table = array([ppf_args[0].ppf(otu_cdf_vals, *ppf_args[1:],
size=num_otus) for ppf_args, otu_cdf_vals in zip(methods, U)])
return where(otu_table > 0, otu_table, 0)
def _sample_entity(self, X, mask, E, R, i, var_e, RE, RTE):
_lambda = np.identity(self.n_dim) / var_e
nz_r = mask[:, i, :].nonzero()
nz_c = mask[:, :, i].nonzero()
nnz_r = nz_r[0].size
nnz_c = nz_c[0].size
nnz_all = nnz_r + nnz_c
self.features[:nnz_r] = RE[nz_r]
self.features[nnz_r:nnz_all] = RTE[nz_c]
self.Y[:nnz_r] = X[:, i, :][nz_r]
self.Y[nnz_r:nnz_all] = X[:, :, i][nz_c]
features = self.features[:nnz_all]
Y = self.Y[:nnz_all]
try:
logit = LogisticRegression(penalty='l2', C=1.0 / var_e, fit_intercept=False)
logit.fit(features, Y)
mu = logit.coef_[0]
prd = logit.predict_proba(features)
_lambda += np.dot(features.T * (prd[:, 0] * prd[:, 1]), features)
except:
mu = np.zeros(self.n_dim)
inv_lambda = np.linalg.inv(_lambda)
E[i] = multivariate_normal(mu, inv_lambda)
def particle_movement(leftwheel, rightwheel, R, robot):
if 'particles' not in robot:
abort(404)
particles = array(robot['particles'])
dd = (leftwheel + rightwheel) / 2
dh = (rightwheel - leftwheel) / ROBOT_RADIUS
z = zeros((len(particles[0]),))
noises = multivariate_normal(z, R, particles.shape[0])
new_particles = zeros(particles.shape)
for i, (particle, noise) in enumerate(zip(particles, noises)):
x = particle[0]
y = particle[1]
h = particle[2]
new_particles[i] = array([
x + dd*cos(h),
y + dd*sin(h),
h + dh
]) + noise
return {
'key': robot['key'],
'type': 'particle',
'particles': new_particles.tolist(),
'mu': mean(new_particles, 0).reshape((-1, 1)).tolist(),
'sigma': cov(new_particles, rowvar=0).tolist()
}
def _sample_relation(self, X, mask, E, R, k, EXE, var_r):
if self.approx_diag:
_lambda = np.ones(self.n_dim ** 2) / var_r
else:
_lambda = np.identity(self.n_dim ** 2) / var_r
kron = EXE[mask[k].flatten() == 1]
Y = X[k][mask[k] == 1].flatten()
if len(np.unique(Y)) == 2:
logit = LogisticRegression(penalty='l2', C=1.0 / var_r, fit_intercept=False)
logit.fit(kron, Y)
mu = logit.coef_[0]
prd = logit.predict_proba(kron)
if self.approx_diag:
_lambda += np.sum(kron.T ** 2 * prd[:, 0] * prd[:, 1], 1)
else:
_lambda += np.dot(kron.T * (prd[:, 0] * prd[:, 1]), kron)
else:
mu = np.zeros(self.n_dim ** 2)
if self.approx_diag:
inv_lambda = 1. / _lambda
R[k] = np.random.normal(mu, inv_lambda).reshape(R[k].shape)
else:
inv_lambda = np.linalg.inv(_lambda)
R[k] = multivariate_normal(mu, inv_lambda).reshape(R[k].shape)
def resample(self, size=None):
"""
Randomly sample a dataset from the estimated pdf.
Parameters
----------
size : int, optional
The number of samples to draw. If not provided, then the size is
the same as the underlying dataset.
Returns
-------
resample : (self.d, `size`) ndarray
The sampled dataset.
"""
if size is None:
size = self.n
norm = transpose(multivariate_normal(zeros((self.d,), float),
self.covariance, size=size))
indices = randint(0, self.n, size=size)
means = self.dataset[:, indices]
return means + norm
def _newQueryStateFromCtm(data, model):
import model.ctm_bohning as ctm
ctm_model = ctm.newModelAtRandom(data, model.K, VocabPrior, model.dtype)
ctm_query = ctm.newQueryState(data, model)
ctm_plan = ctm.newTrainPlan(200, epsilon=1, logFrequency=100, debug=False)
ctm_model, ctm_query, (_, _, _) = ctm.train(data, ctm_model, ctm_query, ctm_plan)
model.vocab[:,:] = ctm_model.vocab
model.topicCov[:,:] = ctm_model.sigT
model.topicMean[:] = ctm_model.topicMean
K, vocab, dtype = model.K, model.vocab, model.dtype
D,T = data.words.shape
assert T == vocab.shape[1], "The number of terms in the document-term matrix (" + str(T) + ") differs from that in the model-states vocabulary parameter " + str(vocab.shape[1])
docLens = np.squeeze(np.asarray(data.words.sum(axis=1)))
outMeans = ctm_query.means
outVarcs = np.ones((D,K), dtype=dtype)
inMeans = np.ndarray(shape=(D,K), dtype=dtype)
for d in range(D):
inMeans[d,:] = rd.multivariate_normal(outMeans[d,:], model.topicCov)
inVarcs = np.ones((D,K), dtype=dtype)
inDocCov = np.ones((D,), dtype=dtype)
return QueryState(outMeans, outVarcs, inMeans, inVarcs, inDocCov, docLens)
def _do_plot_test():
from numpy.random import multivariate_normal
p = np.array([[32, 15],[15., 40.]])
x,y = multivariate_normal(mean=(0,0), cov=p, size=5000).T
sd = 2
a,w,h = covariance_ellipse(p,sd)
print (np.degrees(a), w, h)
count = 0
color=[]
for i in range(len(x)):
if _is_inside_ellipse(x[i], y[i], 0, 0, a, w, h):
color.append('b')
count += 1
else:
color.append('r')
plt.scatter(x,y,alpha=0.2, c=color)
plt.axis('equal')
plot_covariance_ellipse(mean=(0., 0.),
cov = p,
std=sd,
facecolor='none')
print (count / len(x))
def init_filter(self, R, P): self.R = R self.P = P if self.known_init_pos: self.particles = multivariate_normal(self.state, P, N_PARTICLES) self.particles[:,2] = np.random.uniform(-self.bound[4], self.bound[4], N_PARTICLES) self.particles[:,3] = np.random.uniform(-self.bound[5], self.bound[5], N_PARTICLES) else: self.particles = uniform_init(x_low=self.bound[0], x_high=self.bound[1], y_low=self.bound[2], y_high=self.bound[3], v_x=self.bound[4], v_y=self.bound[5], n=N_PARTICLES) self.weights = np.repeat(1/N_PARTICLES, N_PARTICLES) self.state_estimate = np.average(self.particles, axis=0, weights=self.weights) self.z_hat = self.state_estimate[:2]
def _generate_data(cls, n, d, untreated_outcome, treatment_effect, propensity):
"""Generates population data for given untreated_outcome, treatment_effect and propensity functions.
Parameters
----------
n (int): population size
d (int): number of covariates
untreated_outcome (func): untreated outcome conditional on covariates
treatment_effect (func): treatment effect conditional on covariates
propensity (func): probability of treatment conditional on covariates
"""
# Generate covariates
X = multivariate_normal(np.zeros(d), np.diag(np.ones(d)), n)
# Generate treatment
T = np.apply_along_axis(lambda x: binomial(1, propensity(x), 1)[0], 1, X)
# Calculate outcome
Y0 = np.apply_along_axis(lambda x: untreated_outcome(x), 1, X)
treat_effect = np.apply_along_axis(lambda x: treatment_effect(x), 1, X)
Y = Y0 + treat_effect * T
return (X, T, Y)
def predict(self):
""" Predict next position. """
N = self.N
for i, s in enumerate(self.sigmas):
self.sigmas[i] = self.fx(s, self.dt)
e = multivariate_normal(self._mean, self.Q, N)
self.sigmas += e
self.x = np.mean(self.sigmas, axis=0)
P = 0
for y in (self.sigmas - self.x):
P += outer(y, y)
self.P = P / (N - 1)
# save prior
self.x_prior = np.copy(self.x)
self.P_prior = np.copy(self.P)
def spawn(self, errcov=0.0, names=None):
""" generate one LightCurve for which the lightcurve values are the sum of the original ones and gaussian variates from gaussian errors.
"""
# _zylclist = list(self.zylclist) # copy the original list
_zylclist = deepcopy(self.zylclist) # copy the original list
for i in xrange(self.nlc):
e = np.atleast_1d(_zylclist[i][2])
nwant = e.size
ediag = np.diag(e * e)
if errcov == 0.0:
ecovmat = ediag
else:
temp1 = np.repeat(e, nwant).reshape(nwant, nwant)
temp2 = (temp1 * temp1.T - ediag) * errcov
ecovmat = ediag + temp2
et = multivariate_normal(np.zeros_like(e), ecovmat)
_zylclist[i][1] = _zylclist[i][1] + et
if names is None:
names = ["-".join([r, "mock"]) for r in self.names]
return (LightCurve(_zylclist, names=names))
def simulation(rng_seed, n, p, rho):
"""
:param rng_seed: random seed
:param n: number of samples
:param p: number of features
:return: dictionary storing all infos
"""
print("Simulate negative binomial counts regression\n n=%d, p=%d, rho=%3.2f" % (n, p, rho))
seed(rng_seed)
X_mu = normal(0, .1, p)
#X_Sigma = diag(ones(p))
# simulate correlated matrix
temp = np.abs(np.repeat([range(1, p+1)], p, axis=0).transpose() - np.repeat([range(1, p+1)], p, axis=0))
X_Sigma = np.power(rho, temp)
# draw independent samples from MVN(X_mu, X_Sigma)
X = multivariate_normal(X_mu, X_Sigma, n)
# sample from bernoulli and uniform for coefficient beta and model space gamma
opt_gamma = binomial(1, 0.15, p) # model gamma
opt_beta = uniform(-2, 2, p)
# drop all elements in beta whose absolute value less than 0.5
opt_gamma &= abs(opt_beta) > 0.5
opt_beta *= opt_gamma # coefficients
opt_beta0 = 2 # bias
opt_r = 1 # over-dispersion parameter
opt_z = np.dot(X, opt_beta) + opt_beta0
opt_lam = gamma(opt_r, exp(opt_z), n)
y = poisson(opt_lam, n)
opt_omega = expect_omega(opt_z, y, opt_r)
# put everything into a dictionary and return back
negative_binomial_dict = {"X": X, "y": y, "opt_beta": opt_beta, "opt_beta0": opt_beta0,
"opt_r": opt_r, "opt_gamma": opt_gamma, "opt_omega": opt_omega,
"opt_m": np.sum(opt_gamma), "opt_model": np.nonzero(opt_gamma),
"seed": ran_seed}
return negative_binomial_dict
def simulate(self, ts_length=100, random_state=None):
r"""
Simulate a time series of length ts_length, first drawing
.. math::
x_0 \sim N(\mu_0, \Sigma_0)
Parameters
----------
ts_length : scalar(int), optional(default=100)
The length of the simulation
random_state : int or np.random.RandomState, optional
Random seed (integer) or np.random.RandomState instance to set
the initial state of the random number generator for
reproducibility. If None, a randomly initialized RandomState is
used.
Returns
-------
x : array_like(float)
An n x ts_length array, where the t-th column is :math:`x_t`
y : array_like(float)
A k x ts_length array, where the t-th column is :math:`y_t`
"""
random_state = check_random_state(random_state)
x0 = multivariate_normal(self.mu_0.flatten(), self.Sigma_0)
w = random_state.randn(self.m, ts_length-1)
v = self.C.dot(w) # Multiply each w_t by C to get v_t = C w_t
# == simulate time series == #
x = simulate_linear_model(self.A, x0, v, ts_length)
if self.H is not None:
v = random_state.randn(self.l, ts_length)
y = self.G.dot(x) + self.H.dot(v)
else:
y = self.G.dot(x)
return x, y
def create_mock_classification_data(mus, sigs, ns, rseed=None, verbose=False):
'''
Create mock classification dataset using Gaussian distributions
Data contains 'n' points distributed in 'c' classes with 'd' features
TODO: Add feature correlation via correlation matrices
@params:
mus - Length 'c' list of means, each entry is an array of length 'd'
sigs - Length 'c' list of standard deviations, each entry is an array of length 'd'
ns - Length 'c' list of integer number of members of each class
rseed - Random number seed
'''
from numpy import array, diag, append
from numpy.random import seed, multivariate_normal
if verbose:
print('Creating mock classification dataset')
print('Number of features:', len(mus[0]))
print('Number of classes:', len(ns))
print('Total number of entries:', sum(ns))
print()
x = array([])
if rseed is not None: seed(rseed)
for i, (mu, sig, n) in enumerate(zip(mus, sigs, ns)): # mus, sigs, ns must be the same length
if verbose:
print('Class %d members: %d'%(i, n))
print('Mean:', mu)
print('Standard deviation:', sig)
print()
y = multivariate_normal(mean=mu, cov=diag(sig), size=n)
if i==0:
x = y.copy()
else:
x = append(x, y, axis=0)
labels = []
for i, n in enumerate(ns):
labels += n*['c'+str(i)] # Label could be less boring than 'i'
data = {'class': labels}
for i in range(len(ns)):
data['x%d'%(i+1)] = x[:, i]
df = pd.DataFrame.from_dict(data)
df = shuffle(df) # Shuffle entries
return df
def sample_user_hyperparameter(self):
U_bar = np.mean(self.U, axis=0)
S_bar = np.zeros((self.D, self.D))
for i in range(self.N):
S_bar += np.outer(self.U[i, :], self.U[i, :])
beta0 = self.beta0 + self.N
mu0 = (self.beta0 * self.mu0 + self.N * U_bar) / beta0
v0 = self.v0 + self.N
# Note that our choice of W0 is the identity matrix so inverse has no difference
W0_inverse = inv(self.W0) + S_bar + (self.beta0*self.N)/(self.beta0+self.N)\
*np.outer(self.mu0-U_bar, self.mu0-U_bar)
W0 = inv(W0_inverse)
# Sample Lambda_U from Wishart
Lambda_U = wishart.rvs(v0, W0)
# Sample muU from Gaussian
precision = inv(beta0 * Lambda_U)
Mu_U = multivariate_normal(mean=mu0, cov=precision)
return Mu_U, Lambda_U
def Multiple_Descendent_Proposal(particles, y, drift, q, multiple_des=4):
track_prop = []
weight = []
for par in particles:
for k in range(multiple_des):
xt, yt = par
Rt = np.matrix([[-xt, yt], [-yt, -xt]]) / np.sqrt((xt**2 + yt**2))
Zt = random.multivariate_normal(
np.zeros(2), q**2 * np.matrix([[1, 0], [0, k**2]]))
Et = np.array(Rt.T) @ Zt
xnew = xt + drift[0] + Et[0]
ynew = yt + drift[1] + Et[1]
track_prop.append([xnew, ynew])
weight.append(norm.logpdf(f(xnew, ynew)[0], y, scale=delta))
track_prop = np.array(track_prop)
weight = np.array(weight)
weight = weight - max(weight)
weight = np.exp(weight)
weight = weight / np.sum(weight)
#print('dp', np.sum(weight**2))
return track_prop, weight
def multivariate_k(mu, sigma, p, nu):
""" Generate a sample drawn from a multivariate t distribution.
Parameters
----------
mu : 1-d array of size m
mean of the distribution
sigma : 2-d array of size m*m
shape matrix with det = 1
p : float > 0
scale parameter
nu : integer > 0
Degree of freedom of the distribution
Returns
-------
x : 1-d array of size m
sample generated
"""
return mu + multivariate_normal(np.zeros(len(mu)), p * sigma) * np.sqrt(
gamma(nu, 1 / nu))
def null_model(num_samples, dimension=1, rho=0):
data_z = np.reshape(uniform(0, 5, num_samples * dimension),
(num_samples, dimension))
coin_flip_x = np.random.choice([0, 1], replace=True, size=num_samples)
coin_flip_y = np.random.choice([0, 1], replace=True, size=num_samples)
mean_noise = [0, 0]
cov_noise = [[1, 0], [0, 1]]
noise_x, noise_y = multivariate_normal(mean_noise, cov_noise,
num_samples).T
data_x = zeros(num_samples)
data_x[coin_flip_x == 0, ] = 1.7 * data_z[coin_flip_x == 0, 0]
data_x[coin_flip_x == 1, ] = -1.7 * data_z[coin_flip_x == 1, 0]
data_x = data_x + noise_x
data_y = zeros(num_samples)
data_y[coin_flip_y == 0, ] = (data_z[coin_flip_y == 0, 0] - 2.7)**2
data_y[coin_flip_y ==
1, ] = -(data_z[coin_flip_y == 1, 0] - 2.7)**2 + 13
data_y = data_y + noise_y
data_x = np.reshape(data_x, (num_samples, 1))
data_y = np.reshape(data_y, (num_samples, 1))
return data_x, data_y, data_z
def make_data(m=100): # scaled data
from numpy import array
from numpy.random import multivariate_normal
from myutils.datasets import make_legitimate_covariance_matrix
Σ = [
[7, -5, 4, 8], # RSq will be about 0.85
[-5, 14, -9, 0],
[4, -9, 15, -5],
[8, 0, -5, 19]
]
Σ = array(Σ, dtype='i')
Σ = Σ | Σ.transpose()
Σ = make_legitimate_covariance_matrix(
ndim=4) # use this covariance matrix instead of the above one
mx = multivariate_normal(mean=[0, 0, 0, 0], cov=Σ, size=m)
mx = scale(mx)
X, y = split(mx)
return X, y
def main():
r = 0.9999
data = nr.multivariate_normal([0, 0], [[1, r], [r, 1]], 100)
print "Entropy: "
print "Ground Truth = ", log(2 * pi * exp(1)) + 0.5 * log(1 - r**2)
print "LNN: H(X) = ", lnn.entropy(data)
print "KDE: H(X) = ", lnn.KDE_entropy(data)
print "KL: H(X) = ", lnn.KL_entropy(data)
print "LNN(1st order): H(X) = ", lnn.LNN_1_entropy(data), "\n"
print "Mutual Information: "
print "Ground Truth = ", -0.5 * log(1 - r**2)
print "LNN: I(X;Y) = ", lnn.mi(data, split=1)
print "KDE: I(X;Y) = ", lnn._3KDE_mi(data, split=1)
print "3KL: I(X;Y) = ", lnn._3KL_mi(data, split=1)
print "KSG: I(X;Y) = ", lnn._KSG_mi(data, split=1)
print "LNN(1st order): I(X;Y) = ", lnn._3LNN_1_mi(data, split=1)
print "LNN(1st order, KSG trick): I(X;Y) = ", lnn._3LNN_1_KSG_mi(data,
split=1)
print "LNN(2nd order, KSG trick): I(X;Y) = ", lnn._3LNN_2_KSG_mi(data,
split=1)
def predict(self, Q=None):
""" Predict next position. """
if Q == None:
Q = self.Q
if np.isscalar(Q):
Q = eye(self.dim_x) * Q
N = self.N
for i, s in enumerate(self.sigmas):
self.sigmas[i] = self.fx(s, self.dt)
# e = multivariate_normal(self._mean, self.Q, N)
e = multivariate_normal(self._mean, Q, N)
self.sigmas += e
self.x = np.mean(self.sigmas, axis=0)
self.P = outer_product_sum(self.sigmas - self.x) / (N - 1)
# save prior
self.x_prior = np.copy(self.x)
self.P_prior = np.copy(self.P)
def _internal_dynamics(Mprior, Vparent, Achild, Vchild, N=2):
"""
Sample from dynamics conditional of an internal node in tree
:param Mprior: prior of dynamics
:param Vparent: prior column covariance
:param Achild: sum of realization of dynamics of children
:param Vchild: children column covariance
:return: Sample from posterior
"""
assert Mprior.shape == Achild.shape
precision_parent = np.linalg.inv(
np.kron(Vparent, np.eye(Achild[:, 0].size)))
precision_child = np.linalg.inv(np.kron(Vchild, np.eye(Achild[:, 0].size)))
posterior_sigma = np.linalg.inv(precision_parent + N * precision_child)
posterior_mu = posterior_sigma @ (
precision_parent @ Mprior.flatten(order='F')[:, na] +
precision_child @ Achild.flatten(order='F')[:, na])
return npr.multivariate_normal(posterior_mu.flatten(),
posterior_sigma).reshape(Achild.shape,
order='F')
def sample_mog(nsamp, params, component=None, shuffle=False):
"""Sample from a mixture model."""
if component == None:
nums = random.multinomial(nsamp, np.exp(params['logalpha']))
else:
nums = np.zeros(len(params['logalpha']), dtype=int)
nums[component] = nsamp
D = params['sigma'].shape[0]
samples = np.empty((D, nsamp))
cnt = 0
for cmpt in xrange(len(nums)):
mu = params['mu'][:, cmpt]
sigma = params['sigma'][:, :, cmpt]
s = cnt
t = cnt + nums[cmpt]
samples[:, s:t] = random.multivariate_normal(mu, sigma,
(nums[cmpt], )).T
cnt = t
if shuffle:
samples = np.asarray(samples[:, np.random.shuffle(np.arange(nsamp))])
return samples
def pw_normal(n_samples=200, n_bkps=3):
"""Return a 2D piecewise Gaussian signal and the associated changepoints.
Args:
n_samples (int, optional): signal length
n_bkps (int, optional): number of change points
Returns:
tuple: signal of shape (n_samples, 2), list of breakpoints
"""
# breakpoints
bkps = draw_bkps(n_samples, n_bkps)
# we create the signal
signal = np.zeros((n_samples, 2), dtype=float)
cov1 = np.array([[1, 0.9], [0.9, 1]])
cov2 = np.array([[1, -0.9], [-0.9, 1]])
for sub, cov in zip(np.split(signal, bkps), cycle((cov1, cov2))):
n_sub, _ = sub.shape
sub += rd.multivariate_normal([0, 0], cov, size=n_sub)
return signal, bkps
def multivariate_inverse_gaussian(mu, sigma, p, beta):
""" Generate a sample drawn from a multivariate t distribution.
Parameters
----------
mu : 1-d array of size m
mean of the distribution
sigma : 2-d array of size m*m
shape matrix with det = 1
p : float > 0
scale parameter
beta : float > 0
shape parameter
Returns
-------
x : 1-d array of size m
sample generated
"""
return mu + multivariate_normal(np.zeros(len(mu)), p * sigma) * np.sqrt(
wald(1, beta))
def test_uncertainty_correlation(self):
seed(1)
sample_size = 2**15
for expected in [0, 0.75]:
# Make the error distribution
y_true = uniform(0, 1, sample_size)
# Make the errors and uncertainties
draw = multivariate_normal([0, 0], [[1, expected], [expected, 1]],
sample_size)
# Add the errors, and separate out the standard deviations
y_pred = y_true + [d[0] * normal(0, 1) for d in draw]
y_std = [abs(d[1]) for d in draw]
# Test with a very large tolerance for now
measured_corr = uncertainty_correlation(y_true, y_pred, y_std)
corr_error = abs(measured_corr - expected)
self.assertLess(
corr_error, 0.25,
'Error for {:.2f}: {:.2f}'.format(expected, corr_error))
def generate_trajectory(A,
Q,
R,
starting_pt,
depth,
leaf_path,
K,
T,
D_in,
noise=True,
u=None,
D_bias=None):
if u is D_bias is None:
u = np.ones((1, T))
D_bias = 1
x = np.zeros((D_in, T + 1))
x[:, 0] = starting_pt
z = np.zeros(T + 1).astype(int)
for t in range(T):
log_p = compute_leaf_log_prob(R, x[:, t], K, depth, leaf_path)
p_unnorm = np.exp(log_p - np.max(log_p))
p = p_unnorm / np.sum(p_unnorm)
if noise: # Stochastically choose the discrete latent and add noise to continuous latent
choice = npr.multinomial(1, p.ravel(), size=1)
z[t] = np.where(choice[0, :] == 1)[0][0].astype(int)
x[:, t + 1] = (A[:, :-D_bias, z[t]] @ x[:, t][:, na] + \
A[:, -D_bias:, z[t]] @ u[:, t][:, na] + \
npr.multivariate_normal(np.zeros(D_in), Q[:, :, z[t]])[:, na]).flatten()
else: # Use Bayes classifier to choose discrete latent state and add no noise to continuous latent states
z[t] = np.argmax(choice)
x[:, t + 1] = (A[:, :-D_bias, z[t]] @ x[:, t][:, na] + \
A[:, -D_bias:, z[t]] @ u[:, t][:, na]).flatten()
log_p = compute_leaf_log_prob(R, x[:, -1], K, depth, leaf_path)
p_unnorm = np.exp(log_p - np.max(log_p))
p = p_unnorm / np.sum(p_unnorm)
choice = npr.multinomial(1, p.ravel(), size=1)
z[-1] = np.where(choice[0, :] == 1)[0][0]
return x, z
def main():
n = 1000
cov = [[1, 0.9], [0.9, 1]]
beta = 0.9
p_con, p_dis = 0.5, 0.5
gt = (-p_con * 0.5 * log(np.linalg.det(cov)) + p_dis *
(log(2) + beta * log(beta) + (1 - beta) * log(1 - beta)) -
p_con * log(p_con) - p_dis * log(p_dis))
x_con, y_con = nr.multivariate_normal([0, 0], cov, int(n * p_con)).T
x_dis = nr.binomial(1, 0.5, int(n * p_dis))
y_dis = (x_dis + nr.binomial(1, 1 - beta, int(n * p_dis))) % 2
x_dis, y_dis = 2 * x_dis - np.ones(int(n * p_dis)), 2 * y_dis - np.ones(
int(n * p_dis))
x = np.concatenate((x_con, x_dis)).reshape((n, 1))
y = np.concatenate((y_con, y_dis)).reshape((n, 1))
print("Ground Truth = ", gt)
print("Mixed KSG: I(X:Y) = ", mixed.Mixed_KSG(x, y))
print("Partitioning: I(X:Y) = ", mixed.Partitioning(x, y))
print("Noisy KSG: I(X:Y) = ", mixed.Noisy_KSG(x, y))
print("KSG: I(X:Y) = ", mixed.KSG(x, y))
def generate_data(n=400):
INPUT_FEATURES = 2
CLASSES = 3
means = [(-1, 0), (2, 4), (3, 1)]
cov = [diag([1, 1]), diag([0.5, 1.2]), diag([1.5, 0.7])]
alldata = ClassificationDataSet(INPUT_FEATURES, 1, nb_classes=CLASSES)
minX, maxX = means[0][0], means[0][0]
minY, maxY = means[0][1], means[0][1]
for i in range(n):
for klass in range(CLASSES):
features = multivariate_normal(means[klass], cov[klass])
x, y = features
minX, maxX = min(minX, x), max(maxX, x)
minY, maxY = min(minY, y), max(maxY, y)
alldata.addSample(features, [klass])
return {
'minX': minX,
'maxX': maxX,
'minY': minY,
'maxY': maxY,
'd': alldata
}
def initialize(self, x, P):
"""
Initializes the filter with the specified mean and
covariance. Only need to call this if you are using the filter
to filter more than one set of data; this is called by __init__
Parameters
----------
x : np.array(dim_z)
state mean
P : np.array((dim_x, dim_x))
covariance of the state
"""
if x.ndim != 1:
raise ValueError('x must be a 1D array')
self.sigmas = multivariate_normal(mean=x, cov=P, size=self.N)
self.x = x
self.P = P
def test():
data = multivariate_normal([0, 0], [[1, 2], [2, 5]], 100)
### PCA
pc_base = pca(data, base_num = 1)[0]
### Plotting
fig = pl.figure()
fig.add_subplot(1,1,1)
pl.axvline(x=0, color = "#000000")
pl.axhline(y=0, color = "#000000")
### Plot data
pl.scatter(data[:, 0], data[:, 1])
### Draw the 1st principal axis
pc_line = array([-3., 3.]) * (pc_base[1] / pc_base[0])
pl.arrow(0, 0, -pc_base[0] * 2, -pc_base[1] * 2, fc = "r", width = 0.15, head_width = 0.45)
pl.plot([-3, 3], pc_line, "r")
### Settings
pl.xticks(size = 15)
pl.yticks(size = 15)
pl.xlim([-3, 3])
pl.tight_layout()
pl.show()
def setUpClass(cls):
# Generate data
# DGP constants
cls.d = 5
cls.n = 1000
cls.n_test = 200
cls.beta = np.array([0.25, -0.38, 1.41, 0.50, -1.22])
# Test data
cls.X_test = multivariate_normal(
np.zeros(cls.d),
np.diag(np.ones(cls.d)),
cls.n_test)
# Constant treatment effect and propensity
cls.const_te_data = TestMetalearners._generate_data(
cls.n, cls.d, cls._untreated_outcome,
treatment_effect=TestMetalearners._const_te,
propensity=lambda x: 0.3)
# Heterogeneous treatment and propensity
cls.heterogeneous_te_data = TestMetalearners._generate_data(
cls.n, cls.d, cls._untreated_outcome,
treatment_effect=TestMetalearners._heterogeneous_te,
propensity=lambda x: (0.8 if (x[2] > -0.5 and x[2] < 0.5) else 0.2))
def random_mean_and_matrix_semidefinite(self,random_means,random_covariances,array = True, num_points = 1):
'''
Definition:
Parameters
----------
random_means: Matrix
Have in each row a two column vector [(low = a,high = b)]
Example:
means = [[0,1],[2,3],[6,7]]
Where each row indicates in which range the uniform random generator can takes values
random_covariances: Matrix
Is a square matrix, where each row has a column vector, who has inside a vector with two components
Example:
A matrix 3x3.
covariance = [[[a11,b11],[a12,b12],[a13,b13]],[[a21,b21],[a22,b22],[a23,b23]],[[a31,b31],[a32,b32],[a33,b33]]]
Where each row indicates in which range the uniform random generator can takes values
a = [[[1,2],[-5,5],[-100,6]],[[135,683],[2,285],[-135,13]],[[58,135],[16,35],[5,68478]]]
'''
mean = []
for random_mean in random_means:
mean.append(random.uniform(low=random_mean[0],high=random_mean[1]))
if array == True:
covariance = random.rand( random_means.shape[0] , random_means.shape[0])*(random_covariances[1] - random_covariances[0]) + random_covariances[0]
covariance = covariance * covariance.transpose()
else:
covariance = []
for random_covariance in random_covariances:
covariance.append([])
for points in random_covariance:
covariance[-1].append(random.uniform(low=points[0],high=points[1]))
covariance = np.array(covariance)
#A*A' is a semedefinite matrix
covariance = covariance*covariance.transpose()
return [mean,multivariate_normal(mean,covariance,num_points)]
def init_toy_data(num_samples, num_features, num_classes, seed=3):
# num_samples: number of samples *per class*
# num_features: number of features (excluding bias)
# num_classes: number of class labels
# seed: random seed
np.random.seed(seed)
X = np.zeros((num_samples * num_classes, num_features))
y = np.zeros(num_samples * num_classes)
for c in range(num_classes):
# initialize multivariate normal distribution for this class:
# choose a mean for each feature
means = uniform(low=-10, high=10, size=num_features)
# choose a variance for each feature
var = uniform(low=1.0, high=5, size=num_features)
# for simplicity, all features are uncorrelated (covariance between any two features is 0)
cov = var * np.eye(num_features)
# draw samples from normal distribution
X[c * num_samples:c * num_samples +
num_samples, :] = multivariate_normal(means, cov, size=num_samples)
# set label
y[c * num_samples:c * num_samples + num_samples] = c
return X, y
def generate_transformed_data(experiments, reflections, sigma):
from dials_scratch.jmp.stills.potato import PotatoOnEwaldSphere
from numpy.random import multivariate_normal
b1 = 9 / (2 * 0.01)
b2 = 9 / (2 * 0.01)
s0 = matrix.col(experiments[0].beam.get_s0())
s1_obs = flex.vec3_double()
s2_obs = flex.vec3_double()
I_obs = flex.double()
data_list = []
for i in range(len(reflections)):
h = matrix.col(reflections[i]["miller_index"])
s2 = reflections[i]["s1"]
model = PotatoOnEwaldSphere(1 / s0.length(), s2, sigma)
mup = model.conditional_mean()
sigmap = model.conditional_sigma()
scale = model.scale_factor()
data = flex.double(flex.grid(9, 9))
points = multivariate_normal((0, 0),
matrix.sqr(sigmap).as_list_of_lists(),
int(scale * 1000))
for (x, y) in points:
jj = int(4.5 + b1 * y)
ii = int(4.5 + b2 * x)
if ii >= 0 and jj > 0 and ii < data.all()[1] and jj < data.all(
)[0]:
data[jj, ii] += 1
print(i, len(reflections), data.as_numpy_array())
data_list.append(data)
return data_list
def generate_synthetic_logistic_data(n, p, L, blk_nnz, gcov, nstd):
# Generates synthetic data for the logistic regression, using the example
# from [Friedman10]
# n : # of observations
# p : # of predictors
# L : # of blocks
# blk_nnz : # of non-zero coefs. in each block
# gcov : correlation within groups
# nstd : standard deviation of the added noise
# size of each block (assumed to be an integer)
pl = p / L
# generating the coefficients (betas)
coefs = np.zeros((p, 1))
for (i, nnz) in enumerate(blk_nnz):
blkcoefs = np.zeros((pl, 1))
blkcoefs[0:nnz] = np.sign(rand(nnz, 1) - 0.5)
coefs[pl * i:pl * (i + 1)] = permutation(blkcoefs)
# generating the predictors
mu = np.zeros(p)
gsigma = gcov * np.ones((pl, pl))
np.fill_diagonal(gsigma, 1.0)
Sigma = np.kron(np.eye(L), gsigma)
# the predictors come from a standard Gaussian multivariate distribution
X = multivariate_normal(mu, Sigma, n)
# linear function of the explanatory variables in X, plus noise
t = np.dot(X, coefs) + randn(n, 1) * nstd
# applying the logit
Pr = 1 / (1 + np.exp(-t))
# The response variable y[i] is a Bernoulli random variable taking
# value 1 with probability Pr[i]
y = rand(n, 1) <= Pr
# we want each _column_ in X to represent a feature vector
# y and coefs should be also 1D arrays
return X.T, y.flatten(), coefs.flatten()
def MCMC(N,lower_threshold,upper_threshold,rho_Gaussian_Process):
markov_chain = np.array([])
B_0=npr.normal(loc=0,scale=np.sqrt(t))
B = npr.normal(loc=0,scale=np.sqrt(t),size=I0)
X = np.sqrt(rho_correlation)*B_0 + np.sqrt(1-rho_correlation)*B
#(B_0_motion,X) = motion_generator()
#initialize a good x0
while (loss(X)<=lower_threshold):
B_0=npr.normal(loc=0,scale=np.sqrt(t))
B = npr.normal(loc=0,scale=np.sqrt(t),size=I0)
X = np.sqrt(rho_correlation)*B_0 + np.sqrt(1-rho_correlation)*B
for i in range(N):
Y = npr.multivariate_normal(mean=np.zeros(I0),cov=M_cov)
X_intermediate=rho_Gaussian_Process*X+np.sqrt(1-rho_Gaussian_Process**2)*Y
l=loss(X_intermediate)
if (l>lower_threshold):
X=X_intermediate
markov_chain=np.append(markov_chain,l)
else:
markov_chain=np.append(markov_chain,loss(X))
count=len(markov_chain[markov_chain>upper_threshold])
return (markov_chain,count/N)
