FeedforwardNet/codeGenerator/cluster.py at master · microideax/FeedforwardNet · GitHub

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# Author Songyou on 28 June 2018

from sklearn.cluster import KMeans
import numpy as np
from random import randint

def clusters_layers_kmeans(sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, cluster_num):

    # layer_tmp = [len(sub_conv_N)][6]
    # layer_tmp = []
    #
    # for l in range(0, len(sub_conv_N), 1):
    #     layer_tmp[l].append(sub_conv_N[l])
    #     layer_tmp[l].append(sub_conv_M[l])
    #     layer_tmp[l].append(sub_conv_r[l])
    #     layer_tmp[l].append(sub_conv_R[l])
    #     layer_tmp[l].append(sub_conv_K[l])
    #     layer_tmp[l].append(sub_conv_S[l])
    #
    # for l in range(0, len(sub_conv_N), 1):
    #     layer_tmp[l] = np.array(layer_tmp[l], dtype=float)

    sub_conv_N = np.array(sub_conv_N, dtype=int)
    sub_conv_M = np.array(sub_conv_M, dtype=int)
    sub_conv_r = np.array(sub_conv_r, dtype=int)
    sub_conv_R = np.array(sub_conv_R, dtype=int)
    sub_conv_K = np.array(sub_conv_K, dtype=int)
    sub_conv_S = np.array(sub_conv_S, dtype=int)

    # X = np.stack((sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S))
    X = np.stack((sub_conv_N, sub_conv_M, sub_conv_R, sub_conv_K, sub_conv_S))
    #
    # for l in range(0, len(sub_conv_N), 1):
    #     X = np.stack((layer_tmp[l]))

    # http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
    Y = np.transpose(X)

    kmeans = KMeans(n_clusters=cluster_num, random_state=0).fit(Y)

    print Y
    print Y.shape
    print kmeans.labels_
    # print kmeans.predict([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
    print kmeans.cluster_centers_

    return kmeans

def cluster_layer_eudist(sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, cluster_num):
    """
    clustering the layers based on the Euclidean distance between the (N, M) pairs
    :param sub* input params of the extracted layer parameters
    :param cluster_num number of clusters that is expected
    """

    # first: form the (N, M) pairs with a notation of the
    sub_conv_N = np.array(sub_conv_N, dtype=int)
    sub_conv_M = np.array(sub_conv_M, dtype=int)

    return