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import numpy as np
from scipy.signal import triang, tukey

import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context='paper',
        style='ticks',
        font_scale=1,
        rc={'figure.figsize': (8, 5),
            'figure.dpi': 100,  # need fixed

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ROC 全称是“受试者工作特征”（Receiver Operating Characteristic）曲线。体现了不同算法在不同任务下“期望泛化性能”的好坏。

ROC 曲线与画 P-R 曲线的方法完全一样，唯一的区别排序计算的坐标轴改为了“真正例率”（True Postitve Rate，TPR） 和“假正例率”（False Positive Rate，FPR）：

真实情况	预测结果	预测结果
	正例	反例
正例	TP（真正例）	FN（假反例）

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def GroupNeighbor(l):
    """
    Eg: Input <= np.array
    l = np.array([58,59,
                  87,98,
                  101,102,103,104,
                  111,122,
                  133,134,135,136])
    GroupNeighbor(l) = [[58, 59], 
                        [101, 102, 103, 104], 

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## Spiral Memory

# You come across an experimental new kind of memory stored on an infinite two-dimensional grid.

# Each square on the grid is allocated in a spiral pattern starting at a location marked 1 and then counting up while spiraling outward. For example, the first few squares are allocated like this:

# 17 16 15 14 13 
# 18  5  4  3 12 
# 19  6  1  2 11
# 20  7  8  9 10

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#! 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

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

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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)