Python code to train GMM by PyStan.

train_gmm.py

# -*- coding: utf-8 -*-

from sklearn.datasets import make_classification
import numpy as np
import matplotlib.pyplot as plt
import pystan


NUM_MIXTURE_COMPONENTS = 4
NUM_DIMENSIONS = 2


def create_data(num_samples):
    """
    Creates data to train model parameters
    """
    # 乱数で混合重みを生成
    weights = np.random.random(NUM_MIXTURE_COMPONENTS)
    weights = (weights / weights.sum()).tolist()

    feature_vectors, labels = make_classification(
        n_samples=num_samples, n_features=NUM_DIMENSIONS, n_informative=NUM_DIMENSIONS, n_redundant=0,
        n_classes=NUM_MIXTURE_COMPONENTS, n_clusters_per_class=1, weights=weights)

    # グラフ描画用
    plt.scatter(feature_vectors[:, 0], feature_vectors[:, 1], marker='o')
    plt.show()

    return feature_vectors


def main():
    feature_vectors = create_data(1000)

    # create stan model
    compiled_model = pystan.StanModel(file='multi_dimensional_gmm.stan')

    # training
    training_data = dict(N=len(feature_vectors), D=2, M=NUM_MIXTURE_COMPONENTS, X=feature_vectors)
    optimized = compiled_model.optimizing(training_data)

    print(optimized)


if __name__ == '__main__':
    main()