def train(self, features, labels, **kwargs):
allfeatures = np.concatenate(features)
assignments, centroids = select_best_kmeans(allfeatures, self.ks, 1,
"AIC")
histograms = [
assign_centroids(f, centroids, histogram=True, normalise=1)
for f in features
]
base_model = self.base.train(histograms, labels, **kwargs)
return precluster_model(centroids, base_model)
def train(self, features, labels, **kwargs):
allfeatures = np.vstack(features)
assignments, centroids = select_best_kmeans(allfeatures,
self.ks,
repeats=1,
method="AIC",
R=self.R)
histograms = [
assign_centroids(f,
centroids,
histogram=True,
normalise=self.normalise) for f in features
]
base_model = self.base.train(histograms, labels, **kwargs)
return precluster_model(centroids, base_model)
def train(self, features, labels, **kwargs):
allfeatures = np.vstack(features)
assignments, centroids = select_best_kmeans(allfeatures, self.ks, repeats=1, method="AIC", R=self.R)
histograms = [assign_centroids(f, centroids, histogram=True, normalise=self.normalise) for f in features]
base_model = self.base.train(histograms, labels, **kwargs)
return precluster_model(centroids, base_model)
def train(self, features, labels, **kwargs):
allfeatures = np.concatenate(features)
assignments, centroids = select_best_kmeans(allfeatures, self.ks, 1, "AIC")
histograms = [assign_centroids(f, centroids, histogram=True, normalise=1) for f in features]
base_model = self.base.train(histograms, labels, **kwargs)
return precluster_model(centroids, base_model)