def em_alg(data,
init_mu,
init_sigma,
init_mix,
beta_func=None,
num_iter=100,
verbose=False):
'''
Estimate Gaussian mixture models with EM algorithm.
Parameters
----------
data : 2-d array
Data to cluster.
init_mu : 2-d array
Array of initial means. init_mu[k] should provide the mean vector
for the kth group.
init_sigma : list of 2-d arrays or 3-d array
Initial covariance matrices. init_sigma[k] should provide the
covariance matrix for the kth group.
init_mix : array
Initial mixing components.
num_iter : int, optional
Number of EM iterations to run. (default 100)
verbose : bool, optional
Set to true to print status updates.
Returns
-------
res : (curr_mu, curr_sigma, curr_mix)
Final estimates of the mean, covariance, and mixture components.
diag : (logliks, times)
Diagnostics by iteration.
'''
if len(init_mu) != len(init_sigma) or len(init_sigma) != len(init_mix):
raise ValueError('Number of initial values needs to be consistent.')
if not np.isclose(sum(init_mix), 1):
raise ValueError('Initial mixing components should add to 1.')
if beta_func is None:
beta_func = lambda i: 1.
num_groups = len(init_mu)
n = len(data)
try:
p = len(init_mu[0])
except TypeError:
p = 1
curr_mu = init_mu.copy()
curr_sigma = init_sigma.copy()
curr_mix = init_mix.copy()
logliks = np.zeros(num_iter + 1)
time_iter = np.zeros(num_iter + 1)
start_time = process_time()
for iternum in range(num_iter):
if verbose: # Status updates
if np.isclose(iternum // 100, iternum / 100) and iternum != 0:
print()
if np.isclose(iternum // 10, iternum / 10) and iternum != 0:
print('.', end='', flush=True)
beta = beta_func(iternum)
if beta > 1.:
beta = 1.
# E-step
start = process_time()
pdfs = calc_pdfs(data, curr_mu, curr_sigma)
probs = calc_probs(pdfs, curr_mix, beta)
time_iter[iternum + 1] += process_time() - start
probs_raw = calc_probs(pdfs, curr_mix, 1.)
logliks[iternum] = calc_loglik(data, pdfs, probs)
# logliks[iternum] = calc_loglik(data, pdfs, probs_raw)
# M-step
start = process_time()
for k in range(num_groups):
curr_mu[k] = np.average(data, axis=0, weights=probs[:, k])
curr_sigma[k] = np.cov(data,
rowvar=0,
aweights=probs[:, k],
ddof=0)
curr_mix = np.mean(probs, axis=0)
time_iter[iternum + 1] += process_time() - start
logliks[-1] = calc_loglik(data, calc_pdfs(data, curr_mu, curr_sigma),
calc_probs(pdfs, curr_mix, 1.))
times = np.cumsum(time_iter)
return (curr_mu, curr_sigma, curr_mix), (logliks, times)
def em_alg(data, init_mu, init_sigma, init_mix, beta_func=None,
num_iter=100, verbose=False):
'''
Estimate Gaussian mixture models with EM algorithm.
Parameters
----------
data : 2-d array
Data to cluster.
init_mu : 2-d array
Array of initial means. init_mu[k] should provide the mean vector
for the kth group.
init_sigma : list of 2-d arrays or 3-d array
Initial covariance matrices. init_sigma[k] should provide the
covariance matrix for the kth group.
init_mix : array
Initial mixing components.
num_iter : int, optional
Number of EM iterations to run. (default 100)
verbose : bool, optional
Set to true to print status updates.
Returns
-------
res : (curr_mu, curr_sigma, curr_mix)
Final estimates of the mean, covariance, and mixture components.
diag : (logliks, times)
Diagnostics by iteration.
'''
if len(init_mu) != len(init_sigma) or len(init_sigma) != len(init_mix):
raise ValueError(
'Number of initial values needs to be consistent.')
if not np.isclose(sum(init_mix), 1):
raise ValueError(
'Initial mixing components should add to 1.')
if beta_func is None:
beta_func = lambda i: 1.
num_groups = len(init_mu)
n = len(data)
try:
p = len(init_mu[0])
except TypeError:
p = 1
curr_mu = init_mu.copy()
curr_sigma = init_sigma.copy()
curr_mix = init_mix.copy()
logliks = np.zeros(num_iter+1)
time_iter = np.zeros(num_iter+1)
start_time = process_time()
for iternum in range(num_iter):
if verbose: # Status updates
if np.isclose(iternum // 100, iternum / 100) and iternum != 0:
print()
if np.isclose(iternum // 10, iternum / 10) and iternum != 0:
print('.', end='', flush=True)
beta = beta_func(iternum)
if beta > 1.:
beta = 1.
# E-step
start = process_time()
pdfs = calc_pdfs(data, curr_mu, curr_sigma)
probs = calc_probs(pdfs, curr_mix, beta)
time_iter[iternum+1] += process_time() - start
probs_raw = calc_probs(pdfs, curr_mix, 1.)
logliks[iternum] = calc_loglik(data, pdfs, probs)
# logliks[iternum] = calc_loglik(data, pdfs, probs_raw)
# M-step
start = process_time()
for k in range(num_groups):
curr_mu[k] = np.average(data, axis=0, weights=probs[:,k])
curr_sigma[k] = np.cov(data, rowvar=0, aweights=probs[:,k], ddof=0)
curr_mix = np.mean(probs, axis=0)
time_iter[iternum+1] += process_time() - start
logliks[-1] = calc_loglik(
data,
calc_pdfs(data, curr_mu, curr_sigma),
calc_probs(pdfs, curr_mix, 1.)
)
times = np.cumsum(time_iter)
return (curr_mu, curr_sigma, curr_mix), (logliks, times)
def sim_anneal(data,
init_mu,
init_sigma,
init_mix,
temp_func,
num_iter=100,
seed=None,
verbose=False):
'''
Estimate Gaussian mixture models with simulated annealing.
Parameters
----------
data : 2-d array
Data to cluster.
init_mu : 2-d array
Array of initial means. init_mu[k] should provide the mean vector
for the kth group.
init_sigma : list of 2-d arrays or 3-d array
Initial covariance matrices. init_sigma[k] should provide the
covariance matrix for the kth group.
init_mix : array
Initial mixing components.
temp_func : function
Temperature function. Should take an iteration number and return
a temperature.
num_iter : int, optional
Number of EM iterations to run. (default 100)
seed : int, optional
Random seed to initialize algorithm with.
verbose : bool, optional
Set to true to print status updates.
Returns
-------
res : (best_mu, best_sigma, best_mix, best_classes)
Best estimates of the mean, covariance, and mixture components, as
well as the best estimated class memberships.
diag : (logliks, times)
Diagnostics by iteration.
'''
if len(init_mu) != len(init_sigma) or len(init_sigma) != len(init_mix):
raise ValueError('Number of initial values needs to be consistent.')
if not np.isclose(sum(init_mix), 1):
raise ValueError('Initial mixing components should add to 1.')
num_groups = len(init_mu)
n = len(data)
try:
p = len(init_mu[0])
except TypeError:
p = 1
if seed is not None:
np.random.seed(seed)
min_obs = (p + 1) * p / 2 + p
# Initialize arrays
curr_mu = init_mu.copy()
cand_mu = init_mu.copy()
curr_sigma = init_sigma.copy()
cand_sigma = init_sigma.copy()
curr_mix = init_mix.copy()
cand_classes = np.zeros((n, num_groups), dtype=int)
# Randomly assign initial classes
curr_classes = np.random.multinomial(1, curr_mix, size=n)
curr_loglik = calc_loglik(data, calc_pdfs(data, curr_mu, curr_sigma),
curr_classes)
best_loglik = curr_loglik
# Initialize storage arrays
logliks_curr = np.zeros(num_iter + 1)
logliks_best = np.zeros(num_iter + 1)
logliks_curr[0] = curr_loglik
logliks_best[0] = curr_loglik
time_iter = np.zeros(num_iter + 1)
for iternum in range(num_iter):
if verbose: # Status updates
if np.isclose(iternum // 100, iternum / 100) and iternum != 0:
print()
if np.isclose(iternum // 10, iternum / 10) and iternum != 0:
print('.', end='', flush=True)
start = process_time()
# Randomly select new candidate classes and calculate the loglik
pdfs = calc_pdfs(data, curr_mu, curr_sigma)
probs = calc_probs(pdfs, curr_mix, 1.)
check = True
while check:
cand_classes = sample_multinomials(probs)
check = False
for k in range(num_groups):
check = check and np.sum(cand_classes[:, k] == 1) < min_obs
for k in range(num_groups):
cand_mu[k] = np.mean(data[cand_classes[:, k] == 1], axis=0)
cand_sigma[k] = np.cov(data[cand_classes[:, k] == 1], rowvar=0)
cand_loglik = calc_loglik(data, calc_pdfs(data, cand_mu, cand_sigma),
cand_classes)
# Switching
accept = np.random.uniform()
log_accept_prob = (cand_loglik - curr_loglik) / temp_func(iternum)
if cand_loglik >= curr_loglik or np.log(accept) < log_accept_prob:
curr_classes = cand_classes
curr_loglik = cand_loglik
curr_mu = cand_mu
curr_sigma = cand_sigma
curr_mix = np.mean(curr_classes, axis=0)
# Keep best loglik so far
if curr_loglik > best_loglik:
best = (curr_mu, curr_sigma, curr_mix, curr_classes)
best_loglik = curr_loglik
# Storage
time_iter[iternum + 1] += process_time() - start
logliks_curr[iternum + 1] = curr_loglik
logliks_best[iternum + 1] = best_loglik
logliks = {'curr': logliks_curr, 'best': logliks_best}
times = np.cumsum(time_iter)
return best, (logliks, times)
def sim_anneal(data, init_mu, init_sigma, init_mix, temp_func,
num_iter=100, seed=None, verbose=False):
'''
Estimate Gaussian mixture models with simulated annealing.
Parameters
----------
data : 2-d array
Data to cluster.
init_mu : 2-d array
Array of initial means. init_mu[k] should provide the mean vector
for the kth group.
init_sigma : list of 2-d arrays or 3-d array
Initial covariance matrices. init_sigma[k] should provide the
covariance matrix for the kth group.
init_mix : array
Initial mixing components.
temp_func : function
Temperature function. Should take an iteration number and return
a temperature.
num_iter : int, optional
Number of EM iterations to run. (default 100)
seed : int, optional
Random seed to initialize algorithm with.
verbose : bool, optional
Set to true to print status updates.
Returns
-------
res : (best_mu, best_sigma, best_mix, best_classes)
Best estimates of the mean, covariance, and mixture components, as
well as the best estimated class memberships.
diag : (logliks, times)
Diagnostics by iteration.
'''
if len(init_mu) != len(init_sigma) or len(init_sigma) != len(init_mix):
raise ValueError(
'Number of initial values needs to be consistent.')
if not np.isclose(sum(init_mix), 1):
raise ValueError(
'Initial mixing components should add to 1.')
num_groups = len(init_mu)
n = len(data)
try:
p = len(init_mu[0])
except TypeError:
p = 1
if seed is not None:
np.random.seed(seed)
min_obs = (p+1)*p/2 + p
# Initialize arrays
curr_mu = init_mu.copy()
cand_mu = init_mu.copy()
curr_sigma = init_sigma.copy()
cand_sigma = init_sigma.copy()
curr_mix = init_mix.copy()
cand_classes = np.zeros((n, num_groups), dtype=int)
# Randomly assign initial classes
curr_classes = np.random.multinomial(1, curr_mix, size=n)
curr_loglik = calc_loglik(
data, calc_pdfs(data, curr_mu, curr_sigma), curr_classes)
best_loglik = curr_loglik
# Initialize storage arrays
logliks_curr = np.zeros(num_iter+1)
logliks_best = np.zeros(num_iter+1)
logliks_curr[0] = curr_loglik
logliks_best[0] = curr_loglik
time_iter = np.zeros(num_iter+1)
for iternum in range(num_iter):
if verbose: # Status updates
if np.isclose(iternum // 100, iternum / 100) and iternum != 0:
print()
if np.isclose(iternum // 10, iternum / 10) and iternum != 0:
print('.', end='', flush=True)
start = process_time()
# Randomly select new candidate classes and calculate the loglik
pdfs = calc_pdfs(data, curr_mu, curr_sigma)
probs = calc_probs(pdfs, curr_mix, 1.)
check = True
while check:
cand_classes = sample_multinomials(probs)
check = False
for k in range(num_groups):
check = check and np.sum(cand_classes[:,k] == 1) < min_obs
for k in range(num_groups):
cand_mu[k] = np.mean(data[cand_classes[:,k] == 1], axis=0)
cand_sigma[k] = np.cov(data[cand_classes[:,k] == 1], rowvar=0)
cand_loglik = calc_loglik(
data, calc_pdfs(data, cand_mu, cand_sigma), cand_classes)
# Switching
accept = np.random.uniform()
log_accept_prob = (cand_loglik - curr_loglik)/temp_func(iternum)
if cand_loglik >= curr_loglik or np.log(accept) < log_accept_prob:
curr_classes = cand_classes
curr_loglik = cand_loglik
curr_mu = cand_mu
curr_sigma = cand_sigma
curr_mix = np.mean(curr_classes, axis=0)
# Keep best loglik so far
if curr_loglik > best_loglik:
best = (curr_mu, curr_sigma, curr_mix, curr_classes)
best_loglik = curr_loglik
# Storage
time_iter[iternum+1] += process_time() - start
logliks_curr[iternum+1] = curr_loglik
logliks_best[iternum+1] = best_loglik
logliks = {'curr': logliks_curr, 'best': logliks_best}
times = np.cumsum(time_iter)
return best, (logliks, times)