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run.py
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run.py
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#!/usr/bin/env python3
from mlearning.scripts.poly_regression import regress_polynomial
from mlearning.scripts.convolutional_neural_network import convolute
from mlearning.unsupervised.gen_adv_net import Generative_Adversarial_Net
from mlearning.scripts.rbm import start_restricted_bolz_machine as rbm
from mlearning.scripts.genetic_algorithm import genetic_algr
from mlearning.scripts.deep_q_net import start_deepq_net
from mlearning.scripts.dbscan import run_dbscan
from mlearning.scripts.evolved_nn import start_evolved_nn
from mlearning.scripts.k_means import cluster
from mlearning.scripts.svm import cluster_svm
# from mlearning.scripts.naive_bayes import classify_nv_bayes
from mlearning.scripts.pam import cluster_pam
from mlearning.scripts.particle_swam_opt import evolve_pso
from mlearning.scripts.dcgan import dcgan
from mlearning.scripts.perceptron import perceptron
from mlearning.scripts.multilayer_perceptron import m_perceptron
from mlearning.scripts.fp_growth import grow_frequent_pattern
from mlearning.scripts.autoencoder import autoencoder
from mlearning.scripts.adaboost import adaboost
if __name__ == '__main__':
# regress_polynomial()
# convolute()
# Generative_Adversarial_Net()
# rbm()
# genetic_algr()
# start_deepq_net()
# run_dbscan()
# start_evolved_nn()
# cluster()
# cluster_svm()
# classify_nv_bayes()
# cluster_pam()
# evolve_pso()
# dcgan()
# perceptron()
# m_perceptron()
# grow_frequent_pattern()
# autoencoder()
adaboost()