├── BP.py ├── README.md ├── ReadMe.txt ├── TestData_2015.2.20_2015.2.27.txt ├── TrainData_2015.1.1_2015.2.19.txt ├── data_2015_1.txt ├── data_2015_12.txt ├── data_2015_2.txt ├── data_2015_3.txt ├── data_2015_4.txt ├── data_2015_5.txt ├── data_2016_1.txt ├── esc.py ├── input_5flavors_cpu_7days.txt ├── lstm.py ├── lstm_predict.py ├── ploy_fit.py └── predict.py /BP.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | import math 3 | import random 4 | 5 | random.seed(0) 6 | 7 | 8 | def rand(a, b): 9 | return (b - a) * random.random() + a 10 | 11 | 12 | def make_matrix(m, n, fill=0.0): 13 | # type: (object, object, object) -> object 14 | """ 15 | 16 | :type fill: object 17 | """ 18 | mat = [] 19 | for i in range(m): 20 | mat.append([fill] * n) 21 | return mat 22 | 23 | 24 | def sigmoid(x): 25 | return 1.0 / (1.0 + math.exp(-x)) 26 | 27 | 28 | def sigmoid_derivative(x): 29 | return x * (1 - x) 30 | 31 | 32 | def tanh_derivative(values): 33 | return 1. - values ** 2 34 | 35 | 36 | # 定义BPNeuralNetwork类， 使用三个列表维护输入层，隐含层和输出层神经元， 列表中的元素代表对应神经元当前的输出值. 37 | # 使用两个二维列表以邻接矩阵的形式维护输入层与隐含层， 隐含层与输出层之间的连接权值， 通过同样的形式保存矫正矩阵. 38 | class BPNeuralNetwork: 39 | def __init__(self): 40 | self.input_n = 0 # 每层神经元的个数 41 | self.hidden_n = 0 42 | self.output_n = 0 43 | self.input_cells = [] # 三个列表分别表示输入层，隐含层，输出层 当前的输出值 44 | self.hidden_cells = [] 45 | self.output_cells = [] 46 | self.input_weights = [] # 两个权值矩阵 ij,jk 47 | self.output_weights = [] 48 | self.input_correction = [] # 两个矫枉矩阵 49 | self.output_correction = [] 50 | 51 | def setup(self, ni, nh, no): # 定义setup方法初始化神经网络: 52 | self.input_n = ni + 1 53 | self.hidden_n = nh 54 | self.output_n = no 55 | # init cells 56 | self.input_cells = [1] * self.input_n 57 | self.hidden_cells = [1.0] * self.hidden_n 58 | self.output_cells = [1.0] * self.output_n 59 | # init weights 60 | self.input_weights = make_matrix(self.input_n, self.hidden_n) 61 | self.output_weights = make_matrix(self.hidden_n, self.output_n) 62 | # random activate 63 | for i in range(self.input_n): 64 | for h in range(self.hidden_n): 65 | self.input_weights[i][h] = rand(-2.0, 2.0) 66 | # self.input_weights[i][h] = rand(-0.2, 0.2) 67 | for h in range(self.hidden_n): 68 | for o in range(self.output_n): 69 | self.output_weights[h][o] = rand(-2.0, 2.0) 70 | # init correction matrix 71 | self.input_correction = make_matrix(self.input_n, self.hidden_n) 72 | self.output_correction = make_matrix(self.hidden_n, self.output_n) 73 | 74 | def predict(self, inputs): # 一次反馈，计算隐含层和输出层的输出，并返回输出层的输出 75 | # activate input layer 76 | # print "type(input_cells) = ", type(self.input_cells[0]) 77 | # print "type(inputs) = ", type(inputs[0]) 78 | # print "inputs = ", inputs 79 | # print "inputs[0] = ", inputs[0] 80 | 81 | for i in range(self.input_n - 1): 82 | self.input_cells[i] = inputs[i] 83 | # self.input_cells[i] = inputs # 更改 84 | # activate hidden layer 85 | for j in range(self.hidden_n): 86 | total = 0.0 87 | for i in range(self.input_n): 88 | total += self.input_cells[i] * self.input_weights[i][j] 89 | # self.hidden_cells[j] = sigmoid(total) 90 | # self.hidden_cells[j] = math.tanh(total) 91 | self.hidden_cells[j] = 0.01 * total 92 | 93 | # activate output layer 94 | for k in range(self.output_n): 95 | total = 0.0 96 | for j in range(self.hidden_n): 97 | total += self.hidden_cells[j] * self.output_weights[j][k] 98 | # self.output_cells[k] = sigmoid(total) 99 | self.output_cells[k] = 0.01 * total 100 | return self.output_cells[:] 101 | 102 | def back_propagate(self, case, label, learn, correct): # 一次反向传播， 更新权值的全部过程， 并返回误差 103 | # feed forward #case ：输入数据 104 | self.predict(case) 105 | # get output layer error 106 | output_deltas = [0.0] * self.output_n 107 | for o in range(self.output_n): 108 | error = label[o] - self.output_cells[o] 109 | # output_deltas[o] = sigmoid_derivative(self.output_cells[o]) * error 110 | output_deltas[o] = 0.01 * error 111 | # get hidden layer error 112 | hidden_deltas = [0.0] * self.hidden_n 113 | for h in range(self.hidden_n): 114 | error = 0.0 115 | for o in range(self.output_n): 116 | error += output_deltas[o] * self.output_weights[h][o] 117 | # hidden_deltas[h] = sigmoid_derivative(self.hidden_cells[h]) * error 118 | # hidden_deltas[h] = tanh_derivative(self.hidden_cells[h]) * error 119 | hidden_deltas[h] = 0.01 * error 120 | # update output weights 121 | for h in range(self.hidden_n): 122 | for o in range(self.output_n): 123 | change = output_deltas[o] * self.hidden_cells[h] 124 | self.output_weights[h][o] += learn * change + correct * self.output_correction[h][o] 125 | self.output_correction[h][o] = change 126 | # update input weights 127 | for i in range(self.input_n): 128 | for h in range(self.hidden_n): 129 | change = hidden_deltas[h] * self.input_cells[i] 130 | self.input_weights[i][h] += learn * change + correct * self.input_correction[i][h] 131 | self.input_correction[i][h] = change 132 | # get global error 133 | error = 0.0 134 | for o in range(len(label)): 135 | error += 0.5 * abs(label[o] - self.output_cells[o]) 136 | return error 137 | 138 | def train(self, cases, labels, limit=2000, learn=0.05, correct=0.1): # 控制迭代 139 | for j in range(limit): 140 | error = 0.0 141 | for i in range(len(cases)): 142 | label = labels[i] 143 | case = cases[i] 144 | error += self.back_propagate(case, label, learn, correct) 145 | print "error = ", error 146 | 147 | def bp_predict(self, data_x, data_y, pre_x): # 演示 148 | 149 | # 处理数据 150 | x, y = [], [] 151 | for i in range(len(data_x)): 152 | x.append([data_x[i]]) 153 | 154 | predict_x = [] 155 | for i in range(len(pre_x)): 156 | predict_x.append([pre_x[i]]) 157 | 158 | pre_y = [] 159 | self.setup(1, 5, len(data_y[0])) 160 | self.train(x, data_y, 2000, 0.001, 0.1) 161 | for i in range(len(predict_x)): 162 | pre_y.append(self.predict(predict_x[i])) 163 | 164 | print "pre_y = ", pre_y 165 | num_predict = [0] * len(data_y[0]) 166 | 167 | for i in range(len(pre_y)): 168 | for j in range(len(pre_y[0])): 169 | num_predict[j] += pre_y[i][j] 170 | 171 | for i in range(len(num_predict)): 172 | num_predict[i] = int(round(num_predict[i])) 173 | if num_predict[i] < 0: 174 | num_predict[i] = 0 175 | 176 | # print "self.input_weights = ", self.input_weights 177 | # print "self.output_weights = ", self.output_weights 178 | return num_predict 179 | 180 | 181 | if __name__ == '__main__': 182 | data_x = [1, 2, 3, 4] 183 | data_y = [[2], [4], [6], [8]] 184 | pre_x = [7, 8, 9, 10] 185 | # plt.plot() 186 | nn = BPNeuralNetwork() 187 | nn.bp_predict(data_x, data_y, pre_x) 188 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 2018_Huawei_CodeCraft-master 2 | 华为2018年华为软件精英挑战赛相关源码 3 | 4 | ## python版本 5 | python2.7 6 | 7 | ## 开发环境 8 | Pycharm 9 | 10 | ## 程序执行 11 | 本程序使用官方给出的sdk-python文件，只需执行esc.py，线下测试，更改了部分代码，使之能够执行所有练习数据。详情看esc.py文件。具体算法实现位于predict.py，以及BP.py和poltfit.py。 12 | 13 | ## 预测部分 14 | 主要由我编写，先后采用最小二乘，线性回归，多项式曲线拟合，bp神经网络以及Lstm算法。 15 | 16 | ## 分配部分 17 | 由队友w和c编写，分别有firstfit，bestfit以及动态规划法。 18 | 19 | ## 比赛结果 20 | 221 21 | -------------------------------------------------------------------------------- /ReadMe.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liqkjm/2018_Huawei_CodeCraft-master/621f76f45ef8d66a30c808355b866616d60068cc/ReadMe.txt -------------------------------------------------------------------------------- /TestData_2015.2.20_2015.2.27.txt: -------------------------------------------------------------------------------- 1 | 56498dc3-86bc flavor9 2015-02-20 00:20:40 2 | 56498dc4-9a9d flavor7 2015-02-20 00:22:02 3 | 56498dc5-ab0e flavor3 2015-02-20 02:46:26 4 | 56498dc6-8fd8 flavor15 2015-02-20 03:42:58 5 | 56498dc7-ba4b flavor15 2015-02-20 03:42:59 6 | 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56641acd-93e0 flavor2 2016-01-24 21:47:04 681 | 56641ace-862b flavor1 2016-01-24 22:32:07 682 | 56641acf-a372 flavor5 2016-01-25 00:31:41 683 | 56641ad0-91ca flavor13 2016-01-25 01:08:41 684 | 56641ad1-bf21 flavor5 2016-01-25 01:34:11 685 | 56641ad2-986a flavor8 2016-01-25 03:20:06 686 | 56641ad3-965a flavor5 2016-01-25 03:21:10 687 | 56641ad4-8608 flavor8 2016-01-25 03:46:53 688 | 56641ad5-9988 flavor5 2016-01-25 05:03:29 689 | 56641ad6-937a flavor5 2016-01-25 05:06:38 690 | 56641ad7-8a03 flavor5 2016-01-25 05:06:44 691 | 56641ad8-9147 flavor4 2016-01-25 05:08:35 692 | 56641ad9-b564 flavor1 2016-01-25 06:25:09 693 | 56641ada-8522 flavor5 2016-01-25 08:16:39 694 | 56641adb-a6c6 flavor1 2016-01-25 17:56:06 695 | 56641adc-b097 flavor1 2016-01-25 17:56:16 696 | 56641add-a046 flavor5 2016-01-25 17:58:41 697 | 56641ade-bf50 flavor1 2016-01-25 18:00:56 698 | 56641adf-a17f flavor5 2016-01-25 18:02:41 699 | 56641ae0-8b6a flavor1 2016-01-25 18:36:57 700 | 56641ae1-b921 flavor1 2016-01-25 19:08:07 701 | 56641ae2-b02d flavor1 2016-01-25 19:58:53 702 | 56641ae3-97dc flavor7 2016-01-25 20:52:50 703 | 56641ae4-868d flavor14 2016-01-25 21:52:54 704 | 56641ae5-b5c1 flavor5 2016-01-25 22:35:22 705 | 56641ae6-835a flavor1 2016-01-25 22:38:20 706 | 56641ae7-90e8 flavor8 2016-01-25 23:05:38 707 | 56641ae8-83d4 flavor4 2016-01-25 23:24:48 708 | 56641ae9-a4f6 flavor11 2016-01-25 23:35:45 709 | 56641aea-bfd4 flavor9 2016-01-25 23:43:23 710 | 56641aeb-a3c3 flavor11 2016-01-26 00:34:54 711 | 56641aec-80cf flavor12 2016-01-26 00:35:32 712 | 56641aed-b8e9 flavor11 2016-01-26 00:47:38 713 | 56641aee-b77e flavor11 2016-01-26 00:53:16 714 | 56641aef-9c8c flavor8 2016-01-26 01:07:16 715 | 56641af0-833b flavor10 2016-01-26 01:11:00 716 | 56641af1-a0c0 flavor10 2016-01-26 01:12:47 717 | 56641af2-8b35 flavor8 2016-01-26 01:14:57 718 | 56641af3-a26a flavor1 2016-01-26 01:18:57 719 | 56641af4-9d9d flavor8 2016-01-26 02:02:21 720 | 56641af5-aff0 flavor3 2016-01-26 02:04:20 721 | 56641af6-b9df flavor2 2016-01-26 03:22:02 722 | 56641af7-9e75 flavor9 2016-01-26 04:35:01 723 | 56641af8-b071 flavor8 2016-01-26 18:07:12 724 | 56641af9-adb5 flavor2 2016-01-26 18:45:38 725 | 56641afa-88cf flavor2 2016-01-26 18:45:47 726 | 56641afb-adb7 flavor2 2016-01-26 18:45:56 727 | 56641afc-8254 flavor2 2016-01-26 18:46:06 728 | 56641afd-a9c4 flavor15 2016-01-26 19:29:44 729 | 56641afe-9b5b flavor15 2016-01-26 19:31:19 730 | 56641aff-91c4 flavor15 2016-01-26 19:31:25 731 | 56641b00-8c28 flavor12 2016-01-26 19:33:02 732 | 56641b01-9241 flavor8 2016-01-26 19:55:42 733 | 56641b02-8cd2 flavor8 2016-01-26 22:41:27 734 | 56641b03-ae8c flavor3 2016-01-26 22:42:25 735 | 56641b04-b745 flavor9 2016-01-26 22:56:46 736 | 56641b05-a5d4 flavor9 2016-01-26 22:56:47 737 | 56641b06-8087 flavor9 2016-01-26 22:59:04 738 | 56641b07-8ee4 flavor9 2016-01-26 22:59:13 739 | 56641b08-b5ac flavor8 2016-01-26 23:03:40 740 | 56641b09-b12d flavor5 2016-01-26 23:04:40 741 | 56641b0a-b78d flavor3 2016-01-27 00:10:33 742 | 56641b0b-b44c flavor8 2016-01-27 00:13:11 743 | 56641b0c-8b83 flavor4 2016-01-27 00:46:42 744 | 56641b0d-b471 flavor4 2016-01-27 00:47:39 745 | 56641b0e-a0d2 flavor2 2016-01-27 01:09:23 746 | 56641b0f-9535 flavor2 2016-01-27 01:09:26 747 | 56641b10-a8c7 flavor2 2016-01-27 01:09:35 748 | 56641b11-867e flavor2 2016-01-27 01:09:35 749 | 56641b12-a940 flavor5 2016-01-27 01:19:35 750 | 56641b13-b541 flavor5 2016-01-27 01:30:26 751 | 56641b14-ad79 flavor2 2016-01-27 02:47:07 752 | 56641b15-a1f3 flavor1 2016-01-27 06:02:40 753 | 56641b16-bdcf flavor5 2016-01-27 06:38:27 754 | 56641b17-9656 flavor11 2016-01-27 07:00:57 755 | 56641b18-bd79 flavor11 2016-01-27 07:01:22 756 | 56641b19-8117 flavor11 2016-01-27 07:01:35 757 | 56641b1a-a94f flavor11 2016-01-27 07:01:38 758 | 56641b1b-afe5 flavor13 2016-01-27 07:01:41 759 | 56641b1c-a838 flavor8 2016-01-27 07:44:49 760 | 56641b1d-95bb flavor4 2016-01-27 16:23:44 761 | 56641b1e-a348 flavor1 2016-01-27 17:08:28 762 | 56641b1f-a436 flavor4 2016-01-27 17:52:09 763 | 56641b20-81eb flavor20 2016-01-27 18:49:17 764 | 56641b21-9070 flavor12 2016-01-27 19:15:11 765 | 56641b22-9aa6 flavor5 2016-01-27 19:15:23 766 | 56641b23-a962 flavor9 2016-01-27 19:15:23 767 | 56641b24-b2b7 flavor12 2016-01-27 19:15:23 768 | 56641b25-b901 flavor12 2016-01-27 19:15:23 769 | 56641b26-a1e7 flavor12 2016-01-27 19:15:23 770 | 56641b27-9e96 flavor12 2016-01-27 19:15:23 771 | 56641b28-97cd flavor12 2016-01-27 19:15:26 772 | 56641b29-98ac flavor8 2016-01-27 19:54:55 773 | 56641b2a-9d68 flavor8 2016-01-27 19:56:17 774 | 56641b2b-a101 flavor7 2016-01-27 20:00:50 775 | 56641b2c-b7e9 flavor2 2016-01-27 21:48:57 776 | 56641b2d-90bd flavor5 2016-01-27 21:55:00 777 | 56641b2e-abb5 flavor8 2016-01-27 21:55:26 778 | 56641b2f-8fc7 flavor4 2016-01-27 23:01:39 779 | 56641b30-afbc flavor2 2016-01-27 23:14:39 780 | 56641b31-b32d flavor5 2016-01-27 23:19:16 781 | 56641b32-bccd flavor14 2016-01-27 23:39:45 782 | 56641b33-9a15 flavor14 2016-01-27 23:48:45 783 | 56641b34-969d flavor12 2016-01-27 23:56:34 784 | 56641b35-b34a flavor4 2016-01-28 00:12:22 785 | 56641b36-91b4 flavor14 2016-01-28 00:55:57 786 | 56641b37-a4b2 flavor11 2016-01-28 02:14:43 787 | 56641b38-86f3 flavor21 2016-01-28 04:07:51 788 | 56641b39-91a7 flavor1 2016-01-28 06:52:38 789 | 56641b3a-a10b flavor5 2016-01-28 17:06:37 790 | 56641b3b-a451 flavor8 2016-01-28 17:09:57 791 | 56641b3c-b97c flavor11 2016-01-28 18:14:03 792 | 56641b3d-9aed flavor4 2016-01-28 18:27:41 793 | 56641b3e-860a flavor8 2016-01-28 18:31:06 794 | 56641b3f-b6c5 flavor8 2016-01-28 18:31:07 795 | 56641b40-a9cf flavor5 2016-01-28 18:47:42 796 | 56641b41-b0ab flavor8 2016-01-28 18:52:10 797 | 56641b42-956f flavor11 2016-01-28 18:56:57 798 | 56641b43-8bd5 flavor8 2016-01-28 18:58:03 799 | 56641b44-b9f7 flavor11 2016-01-28 18:58:06 800 | 56641b45-a0a6 flavor8 2016-01-28 18:58:12 801 | 56641b46-8acd flavor11 2016-01-28 18:58:15 802 | 56641b47-939a flavor5 2016-01-28 19:09:20 803 | 56641b48-ad3e flavor8 2016-01-28 19:14:50 804 | 56641b49-ae98 flavor7 2016-01-28 19:27:32 805 | 56641b4a-9c1d flavor7 2016-01-28 19:27:36 806 | 56641b4b-b8d9 flavor9 2016-01-28 19:52:38 807 | 56641b4c-b203 flavor8 2016-01-28 20:48:01 808 | 56641b4d-aec7 flavor4 2016-01-28 21:23:32 809 | 56641b4e-b37e flavor7 2016-01-28 21:25:03 810 | 56641b4f-a021 flavor1 2016-01-28 21:47:50 811 | 56641b50-a951 flavor11 2016-01-28 22:01:15 812 | 56641b51-8e8f flavor1 2016-01-28 22:10:59 813 | 56641b52-bbbd flavor6 2016-01-28 22:12:12 814 | 56641b53-864e flavor1 2016-01-28 23:28:03 815 | 56641b54-8f37 flavor8 2016-01-28 23:30:13 816 | 56641b55-8e23 flavor8 2016-01-28 23:33:00 817 | 56641b56-8d28 flavor8 2016-01-28 23:35:09 818 | 56641b57-bac7 flavor8 2016-01-28 23:42:28 819 | 56641b58-b895 flavor5 2016-01-28 23:45:12 820 | 56641b59-bfe3 flavor5 2016-01-28 23:59:31 821 | 56641b5a-91bc flavor4 2016-01-29 00:12:18 822 | 56641b5b-99c6 flavor2 2016-01-29 00:19:44 823 | 56641b5c-b9f6 flavor11 2016-01-29 00:43:48 824 | 56641b5d-bb8c flavor10 2016-01-29 00:43:55 825 | 56641b5e-a7f2 flavor1 2016-01-29 00:47:26 826 | 56641b5f-ac3a flavor9 2016-01-29 01:19:25 827 | 56641b60-9b1d flavor5 2016-01-29 01:39:08 828 | 56641b61-9257 flavor6 2016-01-29 02:28:20 829 | 56641b62-9bdc flavor5 2016-01-29 05:49:08 830 | 56641b63-b111 flavor7 2016-01-29 07:58:22 831 | 56641b64-a367 flavor1 2016-01-29 16:44:50 832 | 56641b65-8ab3 flavor5 2016-01-29 22:09:43 833 | 56641b66-958a flavor1 2016-01-30 05:08:04 834 | 56641b67-93fa flavor1 2016-01-30 07:02:26 835 | 56641b68-b081 flavor2 2016-01-30 08:42:29 836 | 56641b69-ad05 flavor1 2016-01-30 22:27:51 837 | 56641b6a-a6a6 flavor1 2016-01-31 06:23:03 838 | 56641b6b-b0e4 flavor1 2016-01-31 08:56:19 839 | 56641b6c-9805 flavor14 2016-01-31 17:25:18 840 | 56641b6d-9b7d flavor9 2016-01-31 18:50:42 841 | 56641b6e-ab07 flavor9 2016-01-31 18:50:42 842 | 56641b6f-a797 flavor5 2016-01-31 20:38:26 843 | 56641b70-a062 flavor5 2016-01-31 20:55:32 844 | 56641b71-bb40 flavor5 2016-01-31 20:55:44 845 | 56641b72-afd0 flavor5 2016-01-31 20:55:44 846 | 56641b73-b64b flavor1 2016-01-31 21:49:40 847 | -------------------------------------------------------------------------------- /esc.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | import sys 3 | import os 4 | import predict 5 | 6 | 7 | def main(): 8 | 9 | # 训练数据："TrainData_2015.1.1_2015.2.19.txt" 10 | # 测试数据："TestData_2015.2.20_2015.2.27.txt" 11 | # 练习数据： 12 | # "data_2015_2.txt" -> "data_2015_5.txt" 13 | # "data_2015_12.txt" 14 | # "data_2016_1.txt" 15 | file_path = ["TrainData_2015.1.1_2015.2.19.txt", "TestData_2015.2.20_2015.2.27.txt", "data_2015_1.txt", "data_2015_2.txt", "data_2015_3.txt", "data_2015_4.txt", "data_2015_5.txt", 16 | "data_2015_12.txt", "data_2016_1.txt"] 17 | 18 | for ii in range(1): 19 | ii = 7 20 | print "\n第", ii + 1, "组练习数据：",file_path[ii] 21 | print 'main function begin.' 22 | ecs_infor_array = read_lines(file_path[ii]) 23 | input_file_array = read_lines("input_5flavors_cpu_7days.txt") 24 | 25 | # implementation the function predictVm 26 | predic_result = predict.predict_vm(ecs_infor_array, input_file_array) 27 | 28 | # write the result to output file 29 | for i in range(len(predic_result)): 30 | print predic_result[i] 31 | print 'main function end.' 32 | 33 | 34 | def write_result(array, outpuFilePath): 35 | with open(outpuFilePath, 'w') as output_file: 36 | for item in array: 37 | output_file.write("%s\n" % item) 38 | 39 | 40 | def read_lines(file_path): 41 | if os.path.exists(file_path): 42 | array = [] 43 | with open(file_path, 'r') as lines: 44 | for line in lines: 45 | array.append(line) 46 | return array 47 | else: 48 | print 'file not exist: ' + file_path 49 | return None 50 | 51 | 52 | if __name__ == "__main__": 53 | main() 54 | -------------------------------------------------------------------------------- /input_5flavors_cpu_7days.txt: -------------------------------------------------------------------------------- 1 | 56 128 1200 2 | 3 | 5 4 | flavor1 1 1024 5 | flavor2 1 2048 6 | flavor3 1 4096 7 | flavor4 2 2048 8 | flavor5 2 4096 9 | 10 | CPU 11 | 12 | 2015-02-20 00:00:00 13 | 2015-02-27 00:00:00 -------------------------------------------------------------------------------- /lstm.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | import random 4 | 5 | import numpy as np 6 | import math 7 | 8 | 9 | def sigmoid(x): 10 | return 1. / (1 + np.exp(-x)) 11 | 12 | 13 | def sigmoid_derivative(values): # 导数 14 | return values * (1 - values) 15 | 16 | 17 | def tanh_derivative(values): 18 | return 1. - values ** 2 19 | 20 | 21 | # createst uniform random array w/ values in [a,b) and shape args 22 | def rand_arr(a, b, *args): # 随机创建一个数组 23 | np.random.seed(0) 24 | return np.random.rand(*args) * (b - a) + a 25 | 26 | 27 | class LstmParam: 28 | def __init__(self, mem_cell_ct, x_dim): # 初始化 29 | self.mem_cell_ct = mem_cell_ct 30 | self.x_dim = x_dim 31 | concat_len = x_dim + mem_cell_ct # ？ 32 | # weight matrices 33 | self.wg = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) # 创建一个 mem_cell_ct x concat_len 的数组 34 | self.wi = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) 35 | self.wf = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) 36 | self.wo = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len) 37 | # bias terms 38 | self.bg = rand_arr(-0.1, 0.1, mem_cell_ct) 39 | self.bi = rand_arr(-0.1, 0.1, mem_cell_ct) 40 | self.bf = rand_arr(-0.1, 0.1, mem_cell_ct) 41 | self.bo = rand_arr(-0.1, 0.1, mem_cell_ct) 42 | # diffs (derivative of loss function w.r.t. all parameters) 43 | self.wg_diff = np.zeros((mem_cell_ct, concat_len)) 44 | self.wi_diff = np.zeros((mem_cell_ct, concat_len)) 45 | self.wf_diff = np.zeros((mem_cell_ct, concat_len)) 46 | self.wo_diff = np.zeros((mem_cell_ct, concat_len)) 47 | self.bg_diff = np.zeros(mem_cell_ct) 48 | self.bi_diff = np.zeros(mem_cell_ct) 49 | self.bf_diff = np.zeros(mem_cell_ct) 50 | self.bo_diff = np.zeros(mem_cell_ct) 51 | 52 | def apply_diff(self, lr=1): # 应用梯度下降 53 | self.wg -= lr * self.wg_diff 54 | self.wi -= lr * self.wi_diff 55 | self.wf -= lr * self.wf_diff 56 | self.wo -= lr * self.wo_diff 57 | self.bg -= lr * self.bg_diff 58 | self.bi -= lr * self.bi_diff 59 | self.bf -= lr * self.bf_diff 60 | self.bo -= lr * self.bo_diff 61 | # reset diffs to zero 62 | self.wg_diff = np.zeros_like(self.wg) 63 | self.wi_diff = np.zeros_like(self.wi) 64 | self.wf_diff = np.zeros_like(self.wf) 65 | self.wo_diff = np.zeros_like(self.wo) 66 | self.bg_diff = np.zeros_like(self.bg) 67 | self.bi_diff = np.zeros_like(self.bi) 68 | self.bf_diff = np.zeros_like(self.bf) 69 | self.bo_diff = np.zeros_like(self.bo) 70 | 71 | 72 | class LstmState: 73 | def __init__(self, mem_cell_ct, x_dim): 74 | self.g = np.zeros(mem_cell_ct) 75 | self.i = np.zeros(mem_cell_ct) 76 | self.f = np.zeros(mem_cell_ct) 77 | self.o = np.zeros(mem_cell_ct) 78 | self.s = np.zeros(mem_cell_ct) 79 | self.h = np.zeros(mem_cell_ct) 80 | self.bottom_diff_h = np.zeros_like(self.h) # 隐含层的偏导？ 81 | self.bottom_diff_s = np.zeros_like(self.s) 82 | 83 | 84 | class LstmNode: 85 | def __init__(self, lstm_param, lstm_state): 86 | # store reference to parameters and to activations 87 | self.state = lstm_state 88 | self.param = lstm_param 89 | # non-recurrent input concatenated with recurrent input 90 | self.xc = None # 拼接向量 x 与 h 91 | 92 | # 根据当前输入和前一时刻的隐含层，计算底层数据：各种门g，i，f，o，以及cell状态和隐含层h 93 | def bottom_data_is(self, x, s_prev=None, h_prev=None): 94 | # if this is the first lstm node in the network 95 | if s_prev is None: 96 | s_prev = np.zeros_like(self.state.s) 97 | if h_prev is None: 98 | h_prev = np.zeros_like(self.state.h) 99 | # save data for use in backprop 100 | self.s_prev = s_prev # 前一个状态 101 | self.h_prev = h_prev # 前一个隐含层 102 | 103 | # concatenate x(t) and h(t-1)， 即拼接 x(t) 向量和 h(t-1) 向量。 104 | xc = np.hstack((x, h_prev)) 105 | # 计算各种门： 106 | self.state.g = np.tanh(np.dot(self.param.wg, xc) + self.param.bg) 107 | self.state.i = sigmoid(np.dot(self.param.wi, xc) + self.param.bi) 108 | self.state.f = sigmoid(np.dot(self.param.wf, xc) + self.param.bf) 109 | self.state.o = sigmoid(np.dot(self.param.wo, xc) + self.param.bo) 110 | self.state.s = self.state.g * self.state.i + s_prev * self.state.f 111 | self.state.h = self.state.s * self.state.o 112 | 113 | self.xc = xc 114 | 115 | # 计算偏导数： 116 | def top_diff_is(self, top_diff_h, top_diff_s): 117 | # notice that top_diff_s is carried along the constant error carousel 118 | ds = self.state.o * top_diff_h + top_diff_s 119 | do = self.state.s * top_diff_h 120 | di = self.state.g * ds 121 | dg = self.state.i * ds 122 | df = self.s_prev * ds 123 | 124 | # diffs w.r.t. vector inside sigma / tanh function 125 | di_input = sigmoid_derivative(self.state.i) * di 126 | df_input = sigmoid_derivative(self.state.f) * df 127 | do_input = sigmoid_derivative(self.state.o) * do 128 | dg_input = tanh_derivative(self.state.g) * dg 129 | 130 | # diffs w.r.t. inputs 131 | self.param.wi_diff += np.outer(di_input, self.xc) 132 | self.param.wf_diff += np.outer(df_input, self.xc) 133 | self.param.wo_diff += np.outer(do_input, self.xc) 134 | self.param.wg_diff += np.outer(dg_input, self.xc) 135 | self.param.bi_diff += di_input 136 | self.param.bf_diff += df_input 137 | self.param.bo_diff += do_input 138 | self.param.bg_diff += dg_input 139 | 140 | # compute bottom diff 141 | dxc = np.zeros_like(self.xc) 142 | dxc += np.dot(self.param.wi.T, di_input) 143 | dxc += np.dot(self.param.wf.T, df_input) 144 | dxc += np.dot(self.param.wo.T, do_input) 145 | dxc += np.dot(self.param.wg.T, dg_input) 146 | 147 | # save bottom diffs 148 | self.state.bottom_diff_s = ds * self.state.f 149 | self.state.bottom_diff_h = dxc[self.param.x_dim:] 150 | 151 | 152 | class LstmNetwork(): 153 | def __init__(self, lstm_param): # 初始化 154 | self.lstm_param = lstm_param # 所有的要用到的参数 155 | self.lstm_node_list = [] # 节点，包括 156 | # input sequence 157 | self.x_list = [] # 输入队列 158 | 159 | def y_list_is(self, y_list, loss_layer): # 更新误差 160 | """ 161 | Updates diffs by setting target sequence 162 | with corresponding loss layer. 163 | Will *NOT* update parameters. To update parameters, 164 | call self.lstm_param.apply_diff() 165 | """ 166 | assert len(y_list) == len(self.x_list) 167 | idx = len(self.x_list) - 1 # x_list 最后一个x 的下标, 即随着时序的递进，输出 隐含层 h，和目标的误差值。 168 | # first node only gets diffs from label ... 169 | loss = loss_layer.loss(self.lstm_node_list[idx].state.h, y_list[idx]) 170 | diff_h = loss_layer.bottom_diff(self.lstm_node_list[idx].state.h, y_list[idx]) 171 | # here s is not affecting loss due to h(t+1), hence we set equal to zero 172 | diff_s = np.zeros(self.lstm_param.mem_cell_ct) 173 | self.lstm_node_list[idx].top_diff_is(diff_h, diff_s) 174 | idx -= 1 175 | 176 | # ... following nodes also get diffs from next nodes, hence we add diffs to diff_h 177 | # we also propagate error along constant error carousel using diff_s 178 | while idx >= 0: 179 | loss += loss_layer.loss(self.lstm_node_list[idx].state.h, y_list[idx]) 180 | diff_h = loss_layer.bottom_diff(self.lstm_node_list[idx].state.h, y_list[idx]) 181 | diff_h += self.lstm_node_list[idx + 1].state.bottom_diff_h 182 | diff_s = self.lstm_node_list[idx + 1].state.bottom_diff_s 183 | self.lstm_node_list[idx].top_diff_is(diff_h, diff_s) 184 | idx -= 1 185 | 186 | return loss 187 | 188 | def x_list_clear(self): 189 | self.x_list = [] 190 | 191 | def x_list_add(self, x): # 将 x 向量加入到 x_list ，同时根据时序变化，更新各种状态值。 192 | self.x_list.append(x) 193 | if len(self.x_list) > len(self.lstm_node_list): # 添加新的结点和新的cell状态 194 | # need to add new lstm node, create new state mem 195 | lstm_state = LstmState(self.lstm_param.mem_cell_ct, self.lstm_param.x_dim) 196 | self.lstm_node_list.append(LstmNode(self.lstm_param, lstm_state)) 197 | 198 | # get index of most recent x input 199 | idx = len(self.x_list) - 1 200 | if idx == 0: 201 | # no recurrent inputs yet 202 | self.lstm_node_list[idx].bottom_data_is(x) 203 | else: 204 | s_prev = self.lstm_node_list[idx - 1].state.s 205 | h_prev = self.lstm_node_list[idx - 1].state.h 206 | self.lstm_node_list[idx].bottom_data_is(x, s_prev, h_prev) 207 | -------------------------------------------------------------------------------- /lstm_predict.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from lstm import LstmParam, LstmNetwork 5 | 6 | 7 | class ToyLossLayer: 8 | """ 9 | Computes square loss with first element of hidden layer array. 10 | """ 11 | 12 | @classmethod 13 | def loss(self, pred, label): 14 | return (pred[0] - label) ** 2 15 | 16 | @classmethod 17 | def bottom_diff(self, pred, label): 18 | diff = np.zeros_like(pred) 19 | diff[0] = 2 * (pred[0] - label) 20 | return diff 21 | 22 | 23 | def example_0(): 24 | 25 | np.random.seed(0) 26 | mem_cell_ct = 100 27 | 28 | x_dim = 1 29 | lstm_param = LstmParam(mem_cell_ct, x_dim) 30 | lstm_net = LstmNetwork(lstm_param) 31 | 32 | """ 33 | y_list = [-0.5, 0.2, 0.1, -0.5] # 五步 34 | input_val_arr = [np.random.random(x_dim) for _ in y_list] 35 | 36 | for i in range(len(input_val_arr)): 37 | print "input_val_arr = ", input_val_arr[i] 38 | """ 39 | input_val_arr = [ 40 | [1, 2], 41 | [2, 3], 42 | [3, 4] 43 | ] 44 | 45 | pre_x = [ 46 | [3, 4] 47 | ] 48 | 49 | y_list = [0.03, 0.05, 0.07] 50 | x = np.arange(-1, 1, 0.01) 51 | xa = [] 52 | for i in range(len(x)): 53 | xa.append([x[i]]) 54 | # y = 2 * np.sin(x * 2.3) + 0.5 * x ** 3 55 | # y1 = y + 0.5 * (np.random.rand(len(x)) - 0.5) 56 | y = ((x * x - 1) ** 3 + 1) * (np.cos(x * 2) + 0.6 * np.sin(x * 1.3)) 57 | y_list = y + (np.random.rand(len(x)) - 0.5) 58 | # print "input_val_arr = ", input_val_arr 59 | 60 | for cur_iter in range(len(x)): 61 | for ind in range(len(y_list)): 62 | lstm_net.x_list_add(xa[ind]) 63 | 64 | y_pred = [0] * len(y_list) 65 | 66 | for i in range(len(y_list)): 67 | y_pred[i] = lstm_net.lstm_node_list[i].state.h[0] 68 | 69 | loss = lstm_net.y_list_is(y_list, ToyLossLayer) 70 | # print("loss:", "%.3e" % loss) 71 | 72 | lstm_param.apply_diff(lr=0.1) # 更新权重 73 | lstm_net.x_list_clear() 74 | 75 | plt.plot(xa, y_list) 76 | plt.plot(xa, y_pred) 77 | plt.show() 78 | print "loss = ", loss 79 | print "y_pred = ", y_pred 80 | 81 | def test(data_x, day_flaovr_num): 82 | np.random.seed(0) 83 | mem_cell_ct = 100 84 | x_dim = 7 # 7天一个维度 85 | 86 | lstm_param = LstmParam(mem_cell_ct, x_dim) 87 | lstm_net = LstmNetwork(lstm_param) 88 | 89 | """ 90 | input = [] 91 | y_list = [] 92 | # 处理数,将数据处理成 7天一段，逐天滚动 93 | for i in range(len(data_x) - 7): 94 | input.append(data_x[i: i+7]) 95 | # y_list.append(day_flaovr_num[i + 7][0]) 96 | 97 | for i in range(len(data_x) - 7): 98 | y_list.append(day_flaovr_num[i + 7]) 99 | 100 | # y_list = [-0.5, 0.2, 0.1, -0.5] # 五步 101 | # input_val_arr = [np.random.random(x_dim) for _ in y_list] 102 | 103 | for i in range(len(input)): 104 | print "input_val_arr = ", input[i] 105 | 106 | for i in range(len(y_list)): 107 | print "y_list =", y_list[i] 108 | 109 | print "len(input) = ", len(input) 110 | print "len(y_list) = ", len(y_list) 111 | 112 | """ 113 | 114 | input_val_arr = [ 115 | [1, 2, 3, 4, 5, 6, 7], 116 | [2, 3, 4, 5, 6, 7, 8] 117 | [3, 4, 5, 6, 7, 8, 9], 118 | ] 119 | y_list = [1, 2, 3] 120 | for cur_iter in range(100): 121 | # print("iter", "%2s" % str(cur_iter), end=": ") 122 | print "str(cur_iter) = ", str(cur_iter) 123 | for ind in range(len(y_list)): 124 | lstm_net.x_list_add(input_val_arr[ind]) 125 | 126 | # print("y_pred = [" + 127 | # ", ".join(["% 2.5f" % lstm_net.lstm_node_list[ind].state.h[0] for ind in range(len(y_list))]) + 128 | # "]", end=", ") 129 | for i in range(len(y_list)): 130 | print "y_pred = ", lstm_net.lstm_node_list[i].state.h[0] 131 | 132 | loss = lstm_net.y_list_is(y_list, ToyLossLayer) 133 | # print("loss:", "%.3e" % loss) 134 | print "loss = ", loss 135 | lstm_param.apply_diff(lr=0.1) # 更新权重 136 | lstm_net.x_list_clear() 137 | 138 | 139 | if __name__ == "__main__": 140 | example_0() 141 | -------------------------------------------------------------------------------- /ploy_fit.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | 3 | """ 4 | 多项式曲线拟合算法 5 | """ 6 | import matplotlib.pyplot as plt 7 | # import numpy as np 8 | import random 9 | import math 10 | 11 | '阶数为9阶 n=9=k' 12 | order = 5 13 | 14 | 15 | def solve(Vx, Vy, ex, Em, coefficient): 16 | # Em = [] 17 | # 首先计算增广矩阵 18 | for i in range(ex + 1): 19 | Em.append([]) 20 | for j in range(len(Vx)): 21 | Em[i].append(Vx[i][j]) 22 | Em[i].append(Vy[i]) 23 | 24 | # 高斯消元求系数 25 | gauss(Em, coefficient) 26 | 27 | 28 | def rand(a, b): 29 | return (b - a) * random.random() + a 30 | 31 | 32 | def swap(a, b): 33 | t = a 34 | a = b 35 | b = t 36 | 37 | 38 | def gauss(ma, coefficient): 39 | m = ma # 这里主要是方便最后矩阵的显示 40 | row_pos = 0 41 | col_pos = 0 42 | ik = 0 43 | n = len(m) 44 | 45 | # row_pos 变量标记行循环, col_pos 变量标记列循环 46 | # print_matrix("一开始 de 矩阵", m) 47 | 48 | while (row_pos < n) and (col_pos < n): 49 | # print "位置：row_pos = %d, col_pos = %d" % (row_pos, col_pos) 50 | # 选主元 51 | mik = - 1 52 | for i in range(row_pos, n): 53 | if abs(m[i][col_pos]) > mik: 54 | mik = abs(m[i][col_pos]) 55 | ik = i 56 | 57 | if mik == 0.0: 58 | col_pos = col_pos + 1 59 | continue 60 | 61 | # 交换两行 62 | if ik != row_pos: 63 | for j in range(col_pos, n): 64 | swap(m[row_pos][j], m[ik][j]) 65 | swap(m[row_pos][n], m[ik][n]) # 区域之外？ 66 | 67 | try: 68 | # 消元 69 | m[row_pos][n] /= m[row_pos][col_pos] 70 | except ZeroDivisionError: 71 | # 除零异常 一般在无解或无穷多解的情况下出现…… 72 | return 0 73 | 74 | j = n - 1 75 | while j >= col_pos: 76 | m[row_pos][j] /= m[row_pos][col_pos] 77 | j = j - 1 78 | 79 | for i in range(0, n): 80 | if i == row_pos: 81 | continue 82 | m[i][n] -= m[row_pos][n] * m[i][col_pos] 83 | 84 | j = n - 1 85 | while j >= col_pos: 86 | m[i][j] -= m[row_pos][j] * m[i][col_pos] 87 | j = j - 1 88 | 89 | row_pos = row_pos + 1 90 | col_pos = col_pos + 1 91 | 92 | for i in range(0, len(m)): 93 | coefficient[i] = m[i][len(m)] 94 | 95 | 96 | def predict(pre_x, coefficient): 97 | ya = [] 98 | for i in range(0, len(pre_x)): 99 | yy = 0.0 100 | for j in range(0, order + 1): 101 | dy = 1.0 102 | for k in range(0, j): 103 | dy *= pre_x[i] # x^k 104 | # ak*x^k 105 | dy *= coefficient[j] # matAA[j]即为系数a, dy = a * x ^ k 106 | yy += dy # yy = a0 + a1 * x ^1 + a2 * x ^ 2 + ... + an * x ^ n 107 | ya.append(yy) 108 | return ya 109 | 110 | 111 | def ploy_fit(x, y1, pre_x): 112 | # 生成样例曲线上的各个点 100个, x的个数决定了样本量 113 | 114 | Em = [] 115 | coefficient = [0.0] * (order + 1) # 系数 a 116 | nd = [] 117 | 118 | # data = x, y 119 | for i in range(len(x)): 120 | nd.append([x[i], y1[i]]) 121 | 122 | # 进行曲线拟合 AX = Y 123 | matA = [] # 多项式矩阵 X 124 | for i in range(0, order + 1): 125 | matA1 = [] # 每一行 126 | for j in range(0, order + 1): 127 | tx = 0.0 # 每一列 128 | for k in range(0, len(x)): 129 | dx = 1.0 # 表示初始 130 | for l in range(0, j + i): 131 | dx = dx * x[k] # x^k 132 | tx += dx 133 | # 运行n次(len(xa)次)后，tx为sum(x[i]^2k) 或 n (dx=1运行n次) 134 | matA1.append(tx) 135 | matA.append(matA1) 136 | 137 | matB = [] # Y 138 | for k in range(0, order + 1): 139 | ty = 0.0 140 | # 加和n个 141 | for i in range(0, len(x)): 142 | dy = 1.0 143 | # 对于从i=1->n 求 (x[i])^(k-1) 144 | for l in range(0, k): 145 | dy = dy * x[i] # dy即为公式中 (x[i])^k 146 | ty += y1[i] * dy # 先乘完再加和 147 | matB.append(ty) 148 | 149 | # 求 A 150 | solve(matA, matB, order, Em, coefficient) 151 | # predict y 152 | ya = predict(x, coefficient) 153 | y_pred = predict(pre_x, coefficient) 154 | # plt.plot(x, y, '*') 155 | plt.plot(x, ya, 'g') 156 | plt.plot(x, y1, "b") 157 | plt.plot(pre_x, y_pred, color='r') 158 | plt.show() 159 | return y_pred 160 | 161 | 162 | def predict_vm(x, y_list, pre_x): 163 | 164 | num_dict = [] 165 | for v in range(5): 166 | y = [] 167 | for i in range(len(y_list)): 168 | y.append(y_list[i][v]) 169 | temp = 0.0 170 | y_pred = ploy_fit(x, y, pre_x) 171 | for i in range(len(y_pred)): 172 | if y_pred[i] < 0: 173 | y_pred[i] = 0 174 | temp += y_pred[i] 175 | num_dict.append(int(round(temp))) 176 | 177 | return num_dict 178 | 179 | 180 | if __name__ == '__main__': 181 | 182 | x = [1, 2, 3, 4, 5, 6, 7] 183 | y = [9, 1, 8, 2, 7, 3, 6] 184 | ya = ploy_fit(x, y, x) 185 | print "ya = ", ya 186 | 187 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | # -*- coding:UTF-8 -*- 2 | """ 3 | import sys 4 | import time 5 | import string 6 | import math 7 | 8 | """ 9 | from BP import BPNeuralNetwork 10 | import random 11 | import ploy_fit 12 | 13 | random.seed(0) 14 | 15 | 16 | def large_rand(num_dict): 17 | for i in range(len(num_dict)): 18 | num_dict[i] += rand(-3, 5) 19 | num_dict[i] = int(round(num_dict[i])) 20 | if num_dict[i] < 0: 21 | num_dict[i] = 0 22 | print "num_dict = ", num_dict 23 | return num_dict 24 | 25 | 26 | vm_flavorName = ['flavor1', 'flavor2', 'flavor3', 'flavor4', 'flavor5', 'flavor6', 'flavor7', 'flavor8', 'flavor9', 27 | 'flavor10', 'flavor11', 'flavor12', 'flavor13', 'flavor14', 'flavor15'] # 名称 28 | vm_flavorCpu = [1, 1, 1, 2, 2, 2, 4, 4, 4, 8, 8, 8, 16, 16, 16] # CPU 29 | vm_flavorMem = [1, 2, 4, 2, 4, 8, 4, 8, 16, 8, 16, 32, 16, 32, 64] # 内存 30 | recordFlavor = [] 31 | flavorPredict = [] # 初始化预测空列表 32 | numOfPredict = [] 33 | pm_flavor = [] 34 | pm_restCpu = [] # 当前剩余资源 35 | pm_restMem = [] 36 | 37 | 38 | def rand(a, b): 39 | return (b - a) * random.random() + a 40 | 41 | 42 | # 对日期进行解码,如果专业一点,按月份来算天数,每次要多个循环。 43 | def decode(date_str): # 对单独日期进行解码 44 | 45 | date = date_str.split(" ")[0].split("-") 46 | 47 | year = int(date[0]) 48 | month = int(date[1]) 49 | day = int(date[2]) 50 | date_int = (month - 1) * 30 + day 51 | """ 52 | total = 0 53 | m_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] 54 | if (year % 4 == 0 and year % 100 != 0) or year % 400 == 0: 55 | m_month[1] = 29 56 | for i in range(month - 1): 57 | total += m_month[i] 58 | total += day 59 | return total 60 | """ 61 | return date_int 62 | 63 | 64 | # 对所有的日期进行解码 65 | def Decode_Date(date_list): 66 | vm_date_int = [] 67 | for i in range(len(date_list)): 68 | vm_date_int.append(decode(str(date_list[i]))) 69 | 70 | # print "vm_date-int = ", vm_date_int 71 | return vm_date_int 72 | 73 | 74 | # 读取参数,包括物理服务器的信息,以及虚拟机的规格信息和预测起始时间等 75 | def Read_Par(lines): 76 | # type: (object) -> object 77 | global pm_cpu 78 | global pm_mem 79 | global kind 80 | global vm_cpu 81 | global vm_mem 82 | global vm_flavor 83 | 84 | pm = lines[0].split(" ") 85 | pm_cpu = int(pm[0]) 86 | pm_mem = int(pm[1]) 87 | pm_hd = int(pm[2]) 88 | 89 | vm_num = int(lines[2]) 90 | 91 | vm_flavor = [] 92 | vm_cpu = [] 93 | vm_mem = [] 94 | for i in range(vm_num): 95 | line = lines[3 + i].split(" ") 96 | vm_flavor.append(line[0]) 97 | vm_cpu.append(int(line[1])) 98 | vm_mem.append(int(line[2])) 99 | 100 | kind = lines[vm_num + 4] 101 | begin = decode(lines[vm_num + 6]) 102 | end = decode(lines[vm_num + 7]) 103 | predict_day = end - begin + 1 # 预测的总天数 104 | pre_x = [] 105 | for i in range(predict_day): 106 | pre_x.append(begin + i) 107 | return pm_cpu, pm_mem, pm_hd, vm_num, vm_flavor, vm_cpu, vm_mem, kind, begin, end, predict_day, pre_x 108 | 109 | 110 | # 读取测试数据 111 | def Read_Data(lines): 112 | vm_id = [] 113 | vm_f = [] 114 | vm_date = [] 115 | 116 | for i in range(len(lines)): 117 | line = lines[i].split("\t") 118 | vm_id.append(line[0]) 119 | vm_f.append(line[1]) 120 | vm_date.append(line[2]) 121 | 122 | vm_date_int = Decode_Date(vm_date) 123 | return vm_id, vm_f, vm_date_int 124 | 125 | 126 | # 前后几天求和 127 | def sum_begin_to_end(day_flavor_num, sub, begin, end): 128 | total = [] 129 | for i in range(len(day_flavor_num[0])): 130 | temp = 0 131 | for j in range(sub - begin, sub + end, 1): 132 | temp += day_flavor_num[j][i] 133 | total.append(temp) 134 | return total 135 | 136 | 137 | # 判断是否为噪点 138 | def is_noise(date, sub, day_flavor_num): 139 | """ 140 | 判断兼降噪函数, 141 | 142 | """ 143 | # total = [] 144 | # 求前后两周天的总数 145 | if sub > len(day_flavor_num) - 7: # 首尾没有前/后 146 | total = sum_begin_to_end(day_flavor_num, sub, 7, 0) 147 | elif sub < 7: 148 | total = sum_begin_to_end(day_flavor_num, sub, 0, 7) 149 | else: 150 | total = sum_begin_to_end(day_flavor_num, sub, 7, 7) 151 | 152 | for i in range(len(day_flavor_num[0])): 153 | if day_flavor_num[sub][i] > total[i] / 14.0 * 3: # 如果当天大于前后14天的1/2 154 | day_flavor_num[sub][i] = total[i] / 2.0 # 就把它等于2倍的平均数? 155 | 156 | return day_flavor_num[sub] 157 | # print " 降噪之后的输出: ", day_flavor_num[date] 158 | 159 | # noise = [360, 1, 119, 120, 121, 271, 272, 273, 274, 275, 276, 277, 311] # 不行 160 | 161 | ''' 162 | 全国节假日安排: 163 | 元旦:12.30 - 1.1 164 | 春节 165 | 劳动节:4.29 - 5.1 166 | 国庆节:10.1 - 10.7 167 | 双十一:11.11 ! 168 | ''' 169 | 170 | 171 | # 以天为周期统计,其实都一样,不存在什么影响。 172 | def count_flavor(vm_f1, vm_flavor1, vm_date_int): 173 | end = vm_date_int[len(vm_date_int) - 1] 174 | begin = vm_date_int[0] 175 | day_max = end - begin + 1 176 | 177 | day = 0 # 统计一共有多少天, 记住最后天数 + 1(包括 0 ) 178 | day_flavor_num = [] # 二维列表, 统计每一天每规格的个数 179 | day_flavor = [] # 记录相同日期内的所有规格 180 | data_x = [] 181 | 182 | for ii in range(day_max): 183 | data_x.append(begin + ii) 184 | day_flavor_num.append([]) 185 | 186 | # print "data_x = ", data_x 187 | 188 | vm_date_int.append(0) # 添加一个标记点,用于接下来的判断 189 | 190 | # print "vmn_flavor1 = ", vm_flavor1 191 | for r in range(len(vm_date_int) - 1): # 遍历所有日期,把相同日期的规格先放入一个列表, 192 | # 再使用list的count函数,统计每个规格的个数 193 | if vm_date_int[r] != vm_date_int[r + 1]: # 不同日期,天数加一 194 | day_flavor.append(vm_f1[r]) 195 | for i in range(len(vm_flavor1)): # 对列表统计要求的规格的个数 196 | temp = day_flavor.count(vm_flavor1[i]) # 该列表该规格的个数 197 | day_flavor_num[day].append(temp) # 二维列表, 一维为天数, 二维为规格, 其值为 该天该规格的个数 198 | day = day + 1 199 | day_flavor = [] # 重新赋值为空集,开始记录下一天 200 | else: # 与后面一条日期相同,对其进行统计 201 | day_flavor.append(vm_f1[r]) 202 | # print "the day is not end and the day_flavor is : ", day_flavor 203 | # print "day_flavor_num = ", day_flavor_num 204 | # print "len(day_flavor_num) = ", len(day_flavor_num) 205 | print "day = ", day 206 | return day_flavor_num, day 207 | 208 | 209 | # 以天为周期,补上了数据上没有的天数,连2月30号也给补了大概。 210 | def count_day(vm_f, vm_flavor, date): 211 | end_time = date[len(date) - 1] 212 | begin_time = date[0] 213 | day_max = end_time - begin_time + 1 # 总共的天数 214 | day_flavor_num = [] # 二维列表, 统计每一天每种规格的个数 215 | day_flavor = [] # 记录相同日期内的所有规格 216 | data_x = [] # 即 0 -> day_max,!!! 217 | 218 | for i in range(day_max): # 2018年4月5日 16:34:00 day_max 不用减一 219 | data_x.append(begin_time + i) # 没毛病,即使专业点的解码,依旧是这样。只不过总天数会减少。 220 | day_flavor.append([]) 221 | day_flavor_num.append([]) 222 | 223 | # print "data_x = ", data_x 224 | for i in range(day_max): 225 | for j in range(len(date) - 1): 226 | if date[j] == data_x[i]: 227 | day_flavor[i].append(vm_f[j]) 228 | 229 | for i in range(day_max): 230 | for j in vm_flavor: 231 | temp = day_flavor[i].count(j) 232 | day_flavor_num[i].append(temp) 233 | 234 | # print "降噪之前的day_flavor_num = ", day_flavor_num 235 | # plt.plot(data_x, day_flavor_num) 236 | # plt.show() 237 | 238 | for i in range(len(day_flavor_num)): 239 | # for j in range(len(day_flavor_num)): 240 | day_flavor_num[i] = is_noise(data_x[i], i, day_flavor_num) 241 | 242 | # print "降噪之后的day_flavor_num = ", day_flavor_num 243 | # plt.plot(data_x, day_flavor_num) 244 | # plt.show() 245 | return day_flavor_num, day_max, data_x 246 | 247 | 248 | # 以周为周期 249 | def count_week(vm_f, vm_flavor, date): 250 | day_flavor_num, day_max, data_x = count_day(vm_f, vm_flavor, date) 251 | week_max = day_max / 7 # 此处修改:去掉加一,相当于省略了不满一星期的末尾天数 252 | week_flavor_num = [] # 二维列表, 统计每周每规格的个数 253 | week_x = [] # 横坐标,所以,由第一天开始,七天为一个周期,(即使某一天当中,没有申请,也应该把该天列入)。便为其编号为每周第一天的日期除以 7(取整) 254 | week_day = [] # 列表元素为七天的日期 255 | for ii in range(week_max): # 暂时假设跨度最大天数为100天, 初始化二维列表 256 | week_flavor_num.append([]) 257 | week_day.append([]) 258 | 259 | for i in range(week_max): 260 | week_x.append(data_x[0] / 7 + 7 * i) 261 | for j in range(7): 262 | week_day[i].append(data_x[0] + j + i * 7) 263 | 264 | count = 0 265 | for j in range(len(vm_flavor)): 266 | week = 0 267 | for i in range(day_max): 268 | count += day_flavor_num[i][j] 269 | if (i + 1) % 7 == 0: 270 | week_flavor_num[week].append(count) 271 | week += 1 272 | count = 0 273 | # print "week_x = ", week_x 274 | # print "week_day", week_day 275 | # print "week_flavor_num = ", week_flavor_num 276 | return week_max, week_x, week_day, week_flavor_num 277 | 278 | 279 | # 预测方法1:求平均数 280 | def average(vm_flavor, week, week_flavor_num, week_predict): # 预测 281 | num_predict = [] 282 | for ii in range(len(vm_flavor)): 283 | num_predict.append(0) 284 | 285 | ''' 286 | 此处有两种用法:(懒得解决的bug) 287 | 针对于理论上 day = end - begin 288 | 但其实可能没有那么多天的情况(即某些天申请的数量为零,故数据上没有,则没有统计) 289 | 所以: 290 | - 1. day = len(day_flavor_num) 291 | - 2. day < len(day_flavor_num), 可能造成越界 292 | ''' 293 | 294 | for j in range(len(week_flavor_num)): 295 | for k in range(len(vm_flavor)): 296 | num_predict[k] += int(week_flavor_num[j][k]) 297 | 298 | for k in range(len(vm_flavor)): 299 | num_predict[k] = int(round(float(num_predict[k]) / len(week_flavor_num) * week_predict)) 300 | 301 | return num_predict 302 | 303 | 304 | # 预测方法2:最小二乘法 least square method 305 | def compute_mb(data_x, data_y, sub): # 计算 m 和 b 306 | # 横坐标,纵坐标,纵坐标下标,针对不同的规格有不同的 m b 307 | n = len(data_x) 308 | xx_sum = 0.0 # x的平方和 309 | x_sum = 0.0 # x求和 310 | y_sum = 0.0 # y求和 311 | xy_sum = 0.0 # xy的乘积求和 312 | 313 | for i in range(n): 314 | xx_sum += data_x[i] ** 2 315 | y_sum += data_y[i][sub] 316 | x_sum += data_x[i] 317 | xy_sum += data_x[i] * data_y[i][sub] 318 | 319 | b = (xx_sum * y_sum - x_sum * xy_sum) / (n * xx_sum - x_sum * x_sum) 320 | m = (n * xy_sum - x_sum * y_sum) / (n * xx_sum - x_sum ** 2) 321 | 322 | return m, b 323 | 324 | 325 | def predict_lsq(predict_x, m, b): # 预测某种规格,在所给时间段的总申请数量,即 # 预测 y = m * x + b, 326 | # 本次测试用例为一周,predict_x = 1 327 | predict_y = 0.0 328 | for i in range(len(predict_x)): 329 | predict_y += m * predict_x[i] + b 330 | # print "predict_y = ", predict_y 331 | return predict_y 332 | 333 | 334 | def least_sq(data_x, data_y, pre_x, vm_num): # 预测所有规格(numpy库有leastsq函数,还得各种手撸) 335 | # 参数定义:数据横坐标,纵坐标,预测的横坐标,预测的规格种类数量 336 | predict_num = [] # 列表存储所有规格的预测数量 337 | for i in range(vm_num): 338 | m, b = compute_mb(data_x, data_y, i) 339 | pre_y = int(round(predict_lsq(pre_x, m, b))) 340 | if pre_y < 0: 341 | pre_y = 0 342 | predict_num.append(pre_y) 343 | # print "predict_num = ", predict_num 344 | return predict_num 345 | 346 | 347 | # 预测方法3:线性回归-低度下降法(未测试) 348 | def compute_error(b, m, data): # 计算误差 349 | totalError = 0 350 | 351 | for i in range(len(data) - 1): 352 | x = data[i][0] 353 | y = data[i][1] 354 | totalError += (y - (m * x + b)) * (y - (m * x + b)) 355 | 356 | return totalError / float(len(data)) 357 | 358 | 359 | def optimizer(data, starting_b, starting_m, learning_rate, num_iter): 360 | b = starting_b 361 | m = starting_m 362 | 363 | for i in range(num_iter): 364 | b, m = compute_gradient(b, m, data, learning_rate) 365 | return [b, m] 366 | 367 | 368 | def compute_gradient(b_current, m_current, data, learning_rate): # 梯度下降,更新m,b 369 | b_gradient = 0 370 | m_gradient = 0 371 | 372 | N = float(len(data) - 1) 373 | for i in range(len(data) - 1): 374 | x = data[i][0] 375 | y = data[i][1] 376 | # computing partial derivations of our error function 377 | # b_gradient = -(2 / N) * sum((y - (m_current * x + b_current)) ^ 2) 378 | # m_gradient = -(2 / N) * sum(x * (y - (m_current * x + b_current)) ^ 2) 379 | b_gradient += -(2 / N) * (y - ((m_current * x) + b_current)) 380 | m_gradient += -(2 / N) * x * (y - ((m_current * x) + b_current)) 381 | 382 | new_b = b_current - (learning_rate * b_gradient) 383 | new_m = m_current - (learning_rate * m_gradient) 384 | return [new_b, new_m] 385 | 386 | 387 | def Linear_regression(data_x, data_y, pre_x): # data 传入 388 | """ 389 | 处理数据,使之符合函数的要求 390 | """ 391 | num_predict = [0] * len(data_y[0]) 392 | # print "num_predict = ", num_predict 393 | for ii in range(len(data_y[0])): 394 | data = [] 395 | for i in range(len(data_x)): 396 | data.append([]) 397 | for i in range(len(data_x)): 398 | data[i].append(data_x[i]) 399 | data[i].append(data_y[i][ii]) 400 | # print "data = ", data 401 | # 常用的学习率数值:0.01,0.03,0.1,0.3,1,3,10 402 | learning_rate = 0.000001 403 | initial_b = 0.001 404 | initial_m = 0.001 405 | num_iter = 1000 406 | 407 | [b, m] = optimizer(data, initial_b, initial_m, learning_rate, num_iter) 408 | predict_y = predict_lsq(pre_x, m, b) 409 | num_predict[ii] = int(round(predict_y)) 410 | return num_predict 411 | 412 | 413 | # **************************************************************** 414 | # 资源分配 415 | def arrange(flavorPredict, numOfPredict): 416 | global pm_restCpu 417 | global pm_restMem 418 | global recordFlavor 419 | global pm_cpu 420 | global pm_mem 421 | global vm_flavorName 422 | if len(pm_restCpu) == 0: # 第一台pm赋值 423 | pm_restCpu.append(pm_cpu) 424 | pm_restMem.append(pm_mem) 425 | recordFlavor.append([]); # 在第一台pm记录中加入0(用来占位,无实际意义) 426 | index1 = 0 427 | 428 | for i in range(len(flavorPredict)): # 对已预测虚拟机分配 429 | index2 = vm_flavorName.index(flavorPredict[i]) # 确定需要分配的vm类型,取下标 430 | temp1 = numOfPredict[i] # 对应虚拟机数量 431 | while temp1 > 0: # 预测的某种vm数量是0没关系,无法进入循环 432 | # print temp1,"++++++++++++++++" 433 | for index1 in range(len(pm_restCpu)): # 选择哪台pm 434 | # print index1 435 | if (pm_restCpu[index1] < vm_flavorCpu[index2] or pm_restMem[index1] < vm_flavorMem[ 436 | index2]) and index1 + 1 == len(pm_restCpu): # 若放不下且是最后一个物理机 437 | recordFlavor.append([]) # 重新分一台pm 438 | pm_restCpu.append(pm_cpu) # 加物理机 439 | pm_restMem.append(pm_mem) 440 | index1 += 1 # 物理机数量加 1 441 | elif (pm_restCpu[index1] < vm_flavorCpu[index2] or pm_restMem[index1] < vm_flavorMem[ 442 | index2]) and index1 + 1 < len(pm_restCpu): # 当前装不下,但并不是以后的也装不下 443 | continue 444 | 445 | pm_restCpu[index1] -= vm_flavorCpu[index2] # 446 | pm_restMem[index1] -= vm_flavorMem[index2] # 更新剩余men 447 | recordFlavor[index1].append(vm_flavorName[index2]) # 记录某台pm已分配的虚拟机(数量之后统计) 448 | temp1 -= 1 449 | # print index1 450 | # print recordFlavor,"******" 451 | break # 进行下一个vm的分配 452 | 453 | 454 | # 首先是判断下,根据输入文件的kind字段,如果是cpu就针对cpu作优化,如果是内存,就针对内存优化 455 | # 具体策略是:比如kind=cpu,就把预测的的虚拟机和对应的所需个数排序,具体是按照虚拟机的cpu要求来对predictflavor排序,最终目的 456 | # 是得到一个sortedFlavorType列表,这是把预测的所需虚拟机规格按照cpu要求从大到小排列,sortedNumOfPredict是sortedFlavorType列表对应的个数 457 | # 现在只是把虚拟机的被分配顺序改动了,物理资源的分配策略那里没有改动 458 | def arrange_bestfit(flavorPredict, numOfPredict): 459 | print 'bestfit' 460 | global pm_restCpu 461 | global pm_restMem 462 | global recordFlavor 463 | global vm_cpu 464 | global vm_mem 465 | global vm_flavor 466 | global kind 467 | 468 | if len(pm_restCpu) == 0: # 第一台pm赋值 469 | pm_restCpu.append(pm_cpu) 470 | pm_restMem.append(pm_mem) 471 | recordFlavor.append([]) # 在第一台pm记录中加入0(用来占位,无实际意义) 472 | index1 = 0 473 | 474 | sortedFlavorType = [] # 对预测的虚拟机按照kind类型进行排序,是一个嵌套列表 475 | FlavorType = [] # 对预测的虚拟机的对应虚拟机规格的kind需求量(未排序) 476 | 477 | # print '(' + kind + ')' 478 | kind = kind.strip() 479 | 480 | # print '(' + kind + ')' 481 | 482 | if kind == 'MEM': # 如果是内存,就按照虚拟机的内存需求从小到大来对预测出来的虚拟机规格列表排序 483 | # print 'kind = 内存' 484 | for i in flavorPredict: 485 | index2 = vm_flavor.index(i) 486 | FlavorType.append(vm_mem[index2]) 487 | sortFlavorType = zip(flavorPredict, FlavorType) 488 | sortedFlavorType = sorted(sortFlavorType, key=lambda x: x[1], reverse=True) 489 | else: # 如果不是内存,就按照虚拟机的cpu需求从小到大来对预测出来的虚拟机规格列表排序 490 | # print 'kind = CPU' 491 | for i in flavorPredict: 492 | index2 = vm_flavor.index(i) 493 | FlavorType.append(vm_cpu[index2]) 494 | sortFlavorType = zip(flavorPredict, FlavorType) 495 | # for i in sortFlavorType: 496 | # print i 497 | # print "sortFlavorType" 498 | # print sortFlavorType 499 | sortedFlavorType = sorted(sortFlavorType, key=lambda x: x[1], reverse=True) 500 | 501 | # print 'this' 502 | # print sortedFlavorType 503 | 504 | sortedFlavorPredict = [] 505 | sortedNumOfPredict = [] 506 | for i in range(len(sortedFlavorType)): 507 | # print i 508 | temp = sortedFlavorType[i][0] 509 | # print temp 510 | sortedFlavorPredict.append(temp) 511 | # print flavorPredict.index(temp) 512 | tempindex = flavorPredict.index(temp) 513 | sortedNumOfPredict.append(numOfPredict[tempindex]) 514 | # print sortedFlavorPredict # predict demand type(sorted) 515 | # print sortedNumOfPredict # predict demand count(sorted) 516 | 517 | totalFlavor = [] 518 | restFlavor = 0 519 | totalFlavorList = [] 520 | for i in range(len(sortedFlavorPredict)): 521 | # print 'sortedFlavorPredict[i]' 522 | # print sortedFlavorPredict[i] 523 | if (sortedNumOfPredict[i] != 0): 524 | templist = [] 525 | for j in range(sortedNumOfPredict[i]): 526 | totalFlavor.append(sortedFlavorPredict[i]) 527 | restFlavor += 1 528 | templist.append(sortedFlavorPredict[i]) 529 | totalFlavorList.append(templist) 530 | 531 | # print totalFlavor 532 | # print restFlavor 533 | # print totalFlavorList 534 | 535 | while len(totalFlavorList) > 0: # 对所有要放置的虚拟机列表作判断,如果列表不为空,就继续 536 | # print totalFlavor[0][0] 537 | # print vm_flavorName.index(totalFlavorList[0][0]) 538 | index2 = vm_flavorName.index(totalFlavorList[0][0]) # 准备放置虚拟机列表的第一个元素 539 | # print index2 540 | for index1 in range(len(pm_restCpu)): # 对物理机做遍历 541 | if (pm_restCpu[index1] >= vm_flavorCpu[index2]) and (pm_restMem[index1] >= vm_flavorMem[index2]): 542 | pm_restCpu[index1] -= vm_flavorCpu[index2] 543 | pm_restMem[index1] -= vm_flavorMem[index2] 544 | recordFlavor[index1].append(vm_flavorName[index2]) 545 | del (totalFlavor[0]) 546 | del (totalFlavorList[0][0]) 547 | if (len(totalFlavorList[0]) == 0): 548 | del (totalFlavorList[0]) 549 | if (len(totalFlavorList) == 0): 550 | return 551 | row = len(totalFlavorList) 552 | col = len(totalFlavorList[row - 1]) 553 | minFlavor = totalFlavorList[row - 1][col - 1] 554 | minindex = vm_flavorName.index(minFlavor) 555 | # print minFlavor 556 | # print minindex 557 | while len(totalFlavorList) > 0 and (pm_restCpu[index1] > vm_flavorCpu[minindex]) and ( 558 | pm_restMem[index1] > vm_flavorMem[minindex]): 559 | # while (pm_restCpu > 0) and (pm_restMem > 0): 560 | for i in range(len(totalFlavorList)): 561 | index3 = vm_flavorName.index(totalFlavorList[i][0]) 562 | # print index3 563 | if (pm_restCpu[index1] >= vm_flavorCpu[index3]) and ( 564 | pm_restMem[index1] >= vm_flavorMem[index3]): 565 | pm_restCpu[index1] -= vm_flavorCpu[index3] 566 | pm_restMem[index1] -= vm_flavorMem[index3] 567 | recordFlavor[index1].append(vm_flavorName[index3]) 568 | del (totalFlavorList[i][0]) 569 | if (len(totalFlavorList[i]) == 0): 570 | del (totalFlavorList[i]) 571 | if (len(totalFlavorList) == 0): 572 | return 573 | break 574 | row = len(totalFlavorList) 575 | # print row 576 | # print len(totalFlavorList[row - 1]) 577 | col = len(totalFlavorList[row - 1]) 578 | minFlavor = totalFlavorList[row - 1][col - 1] 579 | minindex = vm_flavorName.index(minFlavor) 580 | # print minFlavor 581 | # print minindex 582 | else: 583 | if index1 + 1 == len(pm_restCpu): 584 | recordFlavor.append([]) # 重新分一台pm 585 | pm_restCpu.append(pm_cpu) # 加物理机 586 | pm_restMem.append(pm_mem) 587 | index1 += 1 # 物理机数量加 1 588 | else: 589 | continue 590 | 591 | 592 | def dynamic_programming(flavorPredict, numOfPredict): 593 | global pm_restCpu 594 | global pm_restMem 595 | global recordFlavor 596 | global pm_cpu 597 | global pm_mem 598 | global vm_flavorName 599 | flag = 1 600 | index1 = 0 601 | while flag == 1: 602 | 603 | flag = 0 # 若不改,下个循环退出 604 | 605 | if len(pm_restCpu) == 0: # pm赋值 606 | pm_restCpu.append(pm_cpu) 607 | pm_restMem.append(pm_mem) 608 | recordFlavor.append([]) # 在第一台pm记录中加入空列表 609 | # 将存储的预测结果存入一维列表 610 | 611 | wc = [0] # cpu大小列表 612 | wm = [0] # mem大小列表 613 | n = 0 # 计数待分配的总数 614 | for i in range(len(flavorPredict)): 615 | if numOfPredict[i] > 0: # 预测结果大于0 616 | for j in range(numOfPredict[i]): 617 | index2 = vm_flavorName.index(flavorPredict[i]) 618 | wc.append(vm_flavorCpu[index2]) # 添加 619 | wm.append(vm_flavorMem[index2]) # 620 | n += 1 # 自增计数 621 | 622 | x = [0] * (n + 1) # 初始化,n+1个0 623 | # 以上为初始状态,只有一台pm 624 | pm_cpu1 = pm_cpu 625 | pm_mem1 = pm_mem # 防止改变,中间变量 626 | mc = [] # m[i][j]剩余CPU容量j下,从第i个货物到第n个货物最大装载重量 627 | mm = [] # MEM 628 | for i in range(n + 1): 629 | mc.append([0] * (pm_cpu1 + 1)) # 第一行初始化为0,存储已占用CPU 630 | mm.append([0] * (pm_mem1 + 1)) # 初始化,存储已占用mem 631 | 632 | # print mm,"+++++++++++++++" 633 | if pm_cpu >= vm_flavorCpu[index2] and pm_mem >= vm_flavorMem[ 634 | index2]: # 能装下, 这里下标0在循环中要改 635 | pm_restCpu_max = wc[n] - 1 # 记录mc[],mm[]两个表前几个为0, 636 | else: 637 | pm_restCpu_max = pm_cpu # 全为0 638 | 639 | for j in range(pm_restCpu_max + 1): 640 | mc[n][j] = 0 641 | mm[n][j] = 0 642 | 643 | for j in range(wc[n], pm_cpu1 + 1): 644 | mc[n][j] = wc[n] # d对于物体n 645 | mm[n][j] = wm[n] 646 | 647 | for i in range(n - 1, 0, -1): # 倒序循环n-1---->0 648 | # pm_restCpu_max = min(wc[ii] - 1, c) 649 | if pm_cpu >= vm_flavorCpu[index2] and pm_mem >= vm_flavorMem[ 650 | index2]: # 能装下, 这里下标0在循环中要改 651 | pm_restCpu_max = wc[i] - 1 # 记录mc[],mm[]两个表前几个为0, 652 | else: 653 | pm_restCpu_max = pm_cpu1 # 全为0 654 | 655 | for j in range(pm_restCpu_max + 1): 656 | mc[i][j] = mc[i + 1][j] 657 | mm[i][j] = mm[i + 1][j] 658 | # for j in range(w[ii], c + 1): 659 | # m[ii][j] = max(m[ii + 1][j], m[ii + 1][j - w[ii]] + w[ii]) 660 | for j in range(wc[i], pm_cpu + 1): 661 | # mc[i][j]=max(mc[i + 1][j], mc[i + 1][j - wc[i]] + wc[i])#自定义max(,) 662 | 663 | if (mc[i + 1][j] < mc[i + 1][j - wc[i]] + wc[i]) and (mm[i + 1][j - wm[i]] + wm[i] <= pm_mem): # 664 | 665 | mc[i][j] = mc[i + 1][j - wc[i]] + wc[i] 666 | mm[i][j] = mm[i + 1][j - wc[i]] + wm[i] 667 | else: # 放不下 668 | mc[i][j] = mc[i + 1][j] 669 | mm[i][j] = mm[i + 1][j] 670 | 671 | for i in range(1, n): # 这里的pm_cpu都是初始值 672 | if mc[i][pm_cpu1] == mc[i + 1][pm_cpu1]: # 没装 673 | x[i] = 0 674 | else: # 装了 675 | x[i] = 1 676 | pm_cpu1 -= wc[i] 677 | 678 | if x[n] == mc[n][pm_cpu1]: # 第n个装不装 679 | x[n] = 0 680 | else: 681 | x[n] = 1 682 | pm_cpu1 -= wc[i] 683 | 684 | # print x ,"x[]" #打印***************************************************** 685 | # 解析x[] 686 | xnum = [0] 687 | for i in range(len(numOfPredict)): 688 | xnum.append(xnum[i] + numOfPredict[i]) # 分布区间若3,2,1则为0,3,5,6(<,<=) 689 | 690 | for i in range(1, n + 1): 691 | if x[i] == 1: 692 | for j in range(1, len(xnum)): 693 | if xnum[j - 1] < i <= xnum[j]: 694 | recordFlavor[index1].append(flavorPredict[j - 1]) # 记录某pm内容,index1为物理机序号 695 | 696 | for i in range(len(numOfPredict)): 697 | numOfPredict[i] = 0 # 清零,以便下一个循环记录 698 | # print x,"x[]" 699 | '''调试''' 700 | 701 | # print numOfPredict,"numOfPredict1" 702 | for i in range(1, n + 1): # x[1:n+1] 703 | if x[i] == 0: # 从下标1,若有0,代表没装完虚拟机 704 | flag = 1 705 | 706 | for j in range(1, len(xnum)): 707 | if xnum[j - 1] < i <= xnum[j]: 708 | numOfPredict[j - 1] += 1 # 记录某pm内容 709 | break 710 | # import pdb 711 | # pdb.set_trace()#调试入口 712 | # print numOfPredict,"numOfPredict2" 713 | # print recordFlavor,"recordFlavor" 714 | # print numOfPredict,"numOfPredict" 715 | if flag == 1: 716 | pm_restCpu.append(pm_cpu) 717 | pm_restMem.append(pm_mem) 718 | recordFlavor.append([]) 719 | index1 += 1 # 新开物理机,下标自增 720 | 721 | wc = [0] # cpu大小列表 722 | wm = [0] # mem大小列表 723 | n = 0 # 计数待分配的总数 724 | for i in range(len(flavorPredict)): 725 | if numOfPredict[i] > 0: # 预测结果大于0 726 | for j in range(numOfPredict[i]): 727 | index2 = vm_flavorName.index(flavorPredict[i]) 728 | wc.append(vm_flavorCpu[index2]) # 添加 729 | wm.append(vm_flavorMem[index2]) # 730 | n += 1 # 自增计数 731 | 732 | x = [0] * (n + 1) # 初始化,n+1个0 733 | # print flag,"flag2" 734 | 735 | 736 | # 接口函数 737 | def predict_vm(ecs_lines, input_lines): 738 | # Do your work from here# 739 | result = [] 740 | if ecs_lines is None: 741 | print 'ecs information is none' 742 | return result 743 | if input_lines is None: 744 | print "input file information is none" 745 | return result 746 | global pm_cpu 747 | global pm_mem 748 | global recordFlavor 749 | 750 | # 读取参数 751 | pm_cpu, pm_mem, pm_hd, vm_num, vm_flavor, vm_cpu, \ 752 | vm_mem, kind, begin, end, predict_day, pre_x = Read_Par(input_lines) 753 | 754 | print "predict_day = ", predict_day 755 | print "pre_x = ", pre_x 756 | # 预测时间的总天数 757 | # predict_day = end - begin + 1 # 加1 758 | predict_week = predict_day / 7 759 | # 需要预测的横坐标 760 | 761 | pre_week_x = [] 762 | for i in range(predict_week): 763 | pre_week_x.append(begin / 7 + i) 764 | 765 | # print "pre_week_x = ", pre_week_x 766 | # 读取测试数据 767 | vm_id, vm_f, vm_date_int = Read_Data(ecs_lines) 768 | 769 | # day_flavor_num1, day1 = count_flavor(vm_f, vm_flavor, vm_date_int) 770 | # print "day = ", day1 771 | # print "day_flavor_num = ", day_flavor_num1 772 | 773 | day_flavor_num, day, data_x = count_day(vm_f, vm_flavor, vm_date_int) 774 | 775 | print "day_flavor_num = ", day_flavor_num 776 | # day_flavor_num1, day1, data_x1 = count_flavor(vm_f, vm_flavor, vm_date_int) 777 | 778 | # 线性回归 779 | num_dict = Linear_regression(data_x, day_flavor_num, pre_x) # 得分:63.667 780 | # num_dict = Linear_regression(week_x, week_flavor_num, pre_week_x, vm_num) # 得分:28.511 781 | 782 | # 神经网络 783 | # nn = BPNeuralNetwork() 784 | # num_dict = nn.bp_predict(data_x, day_flavor_num, pre_x) # 60...... 785 | # print "BP_num_dict = ", num_dict 786 | 787 | # ploy_fit 788 | # num_dict = ploy_fit.predict_vm(data_x, day_flavor_num, pre_x) 789 | 790 | # 随机大法 791 | num_dict = large_rand(num_dict) 792 | 793 | total_num = 0 # 记录预测的虚拟机的总数 794 | 795 | for i in range(len(num_dict)): 796 | flavorPredict.append(vm_flavor[i]) # 记录预测虚拟机类型 797 | numOfPredict.append(num_dict[i]) # 记录预测相应虚拟机类型数量 798 | result.append(vm_flavor[i] + " " + str(num_dict[i])) 799 | 800 | for i in range(len(num_dict)): 801 | total_num += numOfPredict[i] 802 | 803 | result.insert(0, total_num) 804 | 805 | # arrange(flavorPredict, numOfPredict) # 调用分配函数 806 | arrange_bestfit(flavorPredict, numOfPredict) 807 | # dynamic_programming(flavorPredict, numOfPredict) 808 | temp = "" 809 | temp2 = '\n' + str(len(recordFlavor)) 810 | 811 | if len(recordFlavor) != 0: # 物理机>0 812 | result.append(temp2) 813 | for i in range(len(recordFlavor)): # 物理机数量 814 | # print i+1, #, 表示不换行 815 | temp = str(i + 1) 816 | # for j in range(len(recordFlavor[i])): #记录的每台物理机分配的虚拟机 817 | for m in range(len(vm_flavorName)): # 一台pm中各种vm计数 818 | count_vm = recordFlavor[i].count(vm_flavorName[m]) 819 | if count_vm > 0: 820 | temp = temp + " " + vm_flavorName[m] + " " + str(count_vm) 821 | result.append(temp) 822 | temp = "" 823 | else: # 物理机==0 824 | result.append(temp2) 825 | 826 | return result 827 | --------------------------------------------------------------------------------