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def affine_forward(x, w, b):
    """
    Inputs:
    - x: A numpy array containing input data, of shape (N, d_1, ..., d_k) 样本
    - w: A numpy array of weights, of shape (D, M) 权重
    - b: A numpy array of biases, of shape (M,) 偏置
    Returns a tuple of:
    - out: output, of shape (N, M)
    - cache: (x, w, b)
    """

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class S_Dbw():
    def __init__(self,data,data_cluster,cluster_centroids_):
        """
        data --> raw data
        data_cluster --> The category that represents each piece of data(the number of category should begin 0)
        cluster_centroids_ --> the center_id of each cluster's center
        """
        self.data = data
        self.data_cluster = data_cluster
        self.cluster_centroids_ = cluster_centroids_

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    def density(self,density_list=[]):
        """
        compute the density of one or two cluster(depend on density_list)
        变量 density_list 将作为此函数的内部列表，其取值范围是0,1,2,... ，元素个数是聚类类别数目
        """
        density = 0
        if len(density_list) == 2:    # 当考虑两个聚类类别时候，给出中心点位置
            center_v = (self.cluster_centroids_[density_list[0]] +self.cluster_centroids_[density_list[1]])/2
        else:                         # 当只考虑某一个聚类类别的时候，给出中心点位置
            center_v = self.cluster_centroids_[density_list[0]]

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    def Dens_bw(self):
        density_list = []
        result = 0
        # 下面的变量 density_list 列表将会算出每个对应单类的密度值。
        for i in range(self.k):
            density_list.append(self.density(density_list=[i])) # i 是循环类别标签
        # 开始循环排列
        for i in range(self.k):
            for j in range(self.k):
                if i==j:

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    def Scat(self):
        # 分母部分：
        sigma_s = np.std(self.data,axis=0)
        sigma_s_2norm = np.sqrt(np.dot(sigma_s.T,sigma_s))

        # 分子部分：
        sum_sigma_2norm = 0
        for i in range(self.k):
            matrix_data_i = self.data[self.data_cluster == i]
            sigma_i = np.std(matrix_data_i,axis=0)

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    def S_Dbw_result(self):
        """
        compute the final result
        """
        return self.Dens_bw()+self.Scat()

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import numpy as np
import S_Dbw as sdbw
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
from sklearn.metrics.pairwise import pairwise_distances_argmin

np.random.seed(0)

S_Dbw_result = []
batch_size = 45

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O  输入训练数据
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)  # 字符数目和单词数目

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iphysresearch

#! usr/bin/python
#coding=utf-8
# http://gree2.github.io/python/2016/05/14/python-with-docker-redis

from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys, os, time