def fit_gmm(self, samples):
"""
Runs a couple of em instances on random starting points and returns
internal GMM representation of best instance
"""
features = RealFeatures(samples.T)
gmms = []
log_likelihoods = zeros(self.num_runs_em)
for i in range(self.num_runs_em):
# set up Shogun's GMM class and run em (corresponds to random
# initialisation)
gmm = GMM(self.num_components)
gmm.set_features(features)
log_likelihoods[i] = gmm.train_em()
gmms.append(gmm)
max_idx = log_likelihoods.argmax()
# construct Gaussian mixture components in internal representation
components = []
for i in range(self.num_components):
mu = gmms[max_idx].get_nth_mean(i)
Sigma = gmms[max_idx].get_nth_cov(i)
components.append(Gaussian(mu, Sigma))
# construct a Gaussian mixture model based on the best EM run
pie = gmms[max_idx].get_coef()
proposal = MixtureDistribution(components[0].dimension,
self.num_components, components,
Discrete(pie))
return proposal
def fit_gmm(self, samples):
"""
Runs a couple of em instances on random starting points and returns
internal GMM representation of best instance
"""
features = RealFeatures(samples.T)
gmms = []
log_likelihoods = zeros(self.num_runs_em)
for i in range(self.num_runs_em):
# set up Shogun's GMM class and run em (corresponds to random
# initialisation)
gmm = GMM(self.num_components)
gmm.set_features(features)
log_likelihoods[i] = gmm.train_em()
gmms.append(gmm)
max_idx = log_likelihoods.argmax()
# construct Gaussian mixture components in internal representation
components = []
for i in range(self.num_components):
mu = gmms[max_idx].get_nth_mean(i)
Sigma = gmms[max_idx].get_nth_cov(i)
components.append(Gaussian(mu, Sigma))
# construct a Gaussian mixture model based on the best EM run
pie = gmms[max_idx].get_coef()
proposal = MixtureDistribution(components[0].dimension,
self.num_components, components,
Discrete(pie))
return proposal
def RunGMMShogun(q):
totalTimer = Timer()
try:
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
dataPoints = np.genfromtxt(self.dataset, delimiter=',')
dataFeat = RealFeatures(dataPoints.T)
# Get all the parameters.
g = re.search("-g (\d+)", options)
n = re.search("-n (\d+)", options)
s = re.search("-n (\d+)", options)
g = 1 if not g else int(g.group(1))
n = 250 if not n else int(n.group(1))
# Create the Gaussian Mixture Model.
model = SGMM(g)
model.set_features(dataFeat)
with totalTimer:
model.train_em(1e-9, n, 1e-9)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def RunGMMShogun(q):
totalTimer = Timer()
try:
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
dataPoints = np.genfromtxt(self.dataset, delimiter=',')
dataFeat = RealFeatures(dataPoints.T)
# Get all the parameters.
g = re.search("-g (\d+)", options)
n = re.search("-n (\d+)", options)
s = re.search("-n (\d+)", options)
g = 1 if not g else int(g.group(1))
n = 250 if not n else int(n.group(1))
# Create the Gaussian Mixture Model.
model = SGMM(g)
model.set_features(dataFeat)
with totalTimer:
model.train_em(1e-9, n, 1e-9)
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def RunGMMShogun():
totalTimer = Timer()
try:
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
dataPoints = np.genfromtxt(self.dataset, delimiter=',')
dataFeat = RealFeatures(dataPoints.T)
# Get all the parameters.
if "gaussians" in options:
g = int(options.pop("gaussians"))
else:
Log.Fatal("Required parameter 'gaussians' not specified!")
raise Exception("missing parameter")
if "max_iterations" in options:
n = int(options.pop("max_iterations"))
else:
n = 0
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
# Create the Gaussian Mixture Model.
model = SGMM(g)
model.set_features(dataFeat)
with totalTimer:
model.train_em(1e-9, n, 1e-9)
except Exception as e:
Log.Info("Exception: " + str(e))
return -1
return totalTimer.ElapsedTime()
def RunGMMShogun():
totalTimer = Timer()
try:
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
dataPoints = np.genfromtxt(self.dataset, delimiter=',')
dataFeat = RealFeatures(dataPoints.T)
# Get all the parameters.
if "gaussians" in options:
g = int(options.pop("gaussians"))
else:
Log.Fatal("Required parameter 'gaussians' not specified!")
raise Exception("missing parameter")
if "max_iterations" in options:
n = int(options.pop("max_iterations"))
else:
n = 0
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
# Create the Gaussian Mixture Model.
model = SGMM(g)
model.set_features(dataFeat)
with totalTimer:
model.train_em(1e-9, n, 1e-9)
except Exception as e:
Log.Info("Exception: " + str(e))
return -1
return totalTimer.ElapsedTime()
