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)