怎么计算一元十五次方程(五十个一元一次方程)
786
2022-05-25
换汤不换药,有手就会
基础肥皂案例
数据集如下:
你的数据只要能跟它合上就行,年份和数据。
你只需要修改的地方:
套上去就完事,完整代码:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
完整文件:
链接:https://pan.baidu.com/s/1FgDKr6ZF__OBuahkpy2PFg?pwd=dat5 提取码:dat5 --来自百度网盘超级会员V3的分享
1
2
3
升级版肥皂案例
数据还是如下:
代码如下,你可以根据自己的数据集修改一下路径罢了:
# coding=utf-8 from pandas import read_csv from pandas import datetime from pandas import concat from pandas import DataFrame from pandas import Series from sklearn.metrics import mean_squared_error from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from math import sqrt from matplotlib import pyplot import numpy # 读取时间数据的格式化 def parser(x): return datetime.strptime(x, '%Y/%m/%d') # 转换成有监督数据 def timeseries_to_supervised(data, lag=1): df = DataFrame(data) columns = [df.shift(i) for i in range(1, lag + 1)] # 数据滑动一格,作为input,df原数据为output columns.append(df) df = concat(columns, axis=1) df.fillna(0, inplace=True) return df # 转换成差分数据 def difference(dataset, interval=1): diff = list() for i in range(interval, len(dataset)): value = dataset[i] - dataset[i - interval] diff.append(value) return Series(diff) # 逆差分 def inverse_difference(history, yhat, interval=1): # 历史数据,预测数据,差分间隔 return yhat + history[-interval] # 缩放 def scale(train, test): # 根据训练数据建立缩放器 scaler = MinMaxScaler(feature_range=(-1, 1)) scaler = scaler.fit(train) # 转换train data train = train.reshape(train.shape[0], train.shape[1]) train_scaled = scaler.transform(train) # 转换test data test = test.reshape(test.shape[0], test.shape[1]) test_scaled = scaler.transform(test) return scaler, train_scaled, test_scaled # 逆缩放 def invert_scale(scaler, X, value): new_row = [x for x in X] + [value] array = numpy.array(new_row) array = array.reshape(1, len(array)) inverted = scaler.inverse_transform(array) return inverted[0, -1] # fit LSTM来训练数据 def fit_lstm(train, batch_size, nb_epoch, neurons): X, y = train[:, 0:-1], train[:, -1] X = X.reshape(X.shape[0], 1, X.shape[1]) model = Sequential() # 添加LSTM层 model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True)) model.add(Dense(1)) # 输出层1个node # 编译,损失函数mse+优化算法adam model.compile(loss='mean_squared_error', optimizer='adam') for i in range(nb_epoch): # 按照batch_size,一次读取batch_size个数据 model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False) model.reset_states() print("当前计算次数:"+str(i)) return model # 1步长预测 def forcast_lstm(model, batch_size, X): X = X.reshape(1, 1, len(X)) yhat = model.predict(X, batch_size=batch_size) return yhat[0, 0] # 加载数据 series = read_csv('data_set/shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser) # 让数据变成稳定的 raw_values = series.values diff_values = difference(raw_values, 1)#转换成差分数据 # 把稳定的数据变成有监督数据 supervised = timeseries_to_supervised(diff_values, 1) supervised_values = supervised.values # 数据拆分:训练数据、测试数据,前24行是训练集,后12行是测试集 train, test = supervised_values[0:-12], supervised_values[-12:] # 数据缩放 scaler, train_scaled, test_scaled = scale(train, test) # fit 模型 lstm_model = fit_lstm(train_scaled, 1, 100, 4) # 训练数据,batch_size,epoche次数, 神经元个数 # 预测 train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)#训练数据集转换为可输入的矩阵 lstm_model.predict(train_reshaped, batch_size=1)#用模型对训练数据矩阵进行预测 # 测试数据的前向验证,实验发现,如果训练次数很少的话,模型回简单的把数据后移,以昨天的数据作为今天的预测值,当训练次数足够多的时候 # 才会体现出来训练结果 predictions = list() for i in range(len(test_scaled)):#根据测试数据进行预测,取测试数据的一个数值作为输入,计算出下一个预测值,以此类推 # 1步长预测 X, y = test_scaled[i, 0:-1], test_scaled[i, -1] yhat = forcast_lstm(lstm_model, 1, X) # 逆缩放 yhat = invert_scale(scaler, X, yhat) # 逆差分 yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i) predictions.append(yhat) expected = raw_values[len(train) + i + 1] print('Moth=%d, Predicted=%f, Expected=%f' % (i + 1, yhat, expected)) # 性能报告 rmse = sqrt(mean_squared_error(raw_values[-12:], predictions)) print('Test RMSE:%.3f' % rmse) # 绘图 pyplot.plot(raw_values[-12:]) pyplot.plot(predictions) pyplot.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
结果如下:
具体自己改改,给个参考。
完整文件:
链接:https://pan.baidu.com/s/1tYDb44Ge5S6Wwt1sPE8iHA?pwd=hkkc 提取码:hkkc --来自百度网盘超级会员V3的分享
1
2
3
数模q un:912166339比赛期间禁止交流,赛后再聊,订阅本专栏,观看更多数学模型套路与分析。
更健壮的LSTM
数据集不变,代码如下:
# coding=utf-8 from pandas import read_csv from pandas import datetime from pandas import concat from pandas import DataFrame from pandas import Series from sklearn.metrics import mean_squared_error from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from math import sqrt from matplotlib import pyplot import numpy # 读取时间数据的格式化 def parser(x): return datetime.strptime(x, '%Y/%m/%d') # 转换成有监督数据 def timeseries_to_supervised(data, lag=1): df = DataFrame(data) columns = [df.shift(i) for i in range(1, lag + 1)] # 数据滑动一格,作为input,df原数据为output columns.append(df) df = concat(columns, axis=1) df.fillna(0, inplace=True) return df # 转换成差分数据 def difference(dataset, interval=1): diff = list() for i in range(interval, len(dataset)): value = dataset[i] - dataset[i - interval] diff.append(value) return Series(diff) # 逆差分 def inverse_difference(history, yhat, interval=1): # 历史数据,预测数据,差分间隔 return yhat + history[-interval] # 缩放 def scale(train, test): # 根据训练数据建立缩放器 scaler = MinMaxScaler(feature_range=(-1, 1)) scaler = scaler.fit(train) # 转换train data train = train.reshape(train.shape[0], train.shape[1]) train_scaled = scaler.transform(train) # 转换test data test = test.reshape(test.shape[0], test.shape[1]) test_scaled = scaler.transform(test) return scaler, train_scaled, test_scaled # 逆缩放 def invert_scale(scaler, X, value): new_row = [x for x in X] + [value] array = numpy.array(new_row) array = array.reshape(1, len(array)) inverted = scaler.inverse_transform(array) return inverted[0, -1] # fit LSTM来训练数据 def fit_lstm(train, batch_size, nb_epoch, neurons): X, y = train[:, 0:-1], train[:, -1] X = X.reshape(X.shape[0], 1, X.shape[1]) model = Sequential() # 添加LSTM层 model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True)) model.add(Dense(1)) # 输出层1个node # 编译,损失函数mse+优化算法adam model.compile(loss='mean_squared_error', optimizer='adam') for i in range(nb_epoch): # 按照batch_size,一次读取batch_size个数据 model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False) model.reset_states() print("当前计算次数:"+str(i)) return model # 1步长预测 def forcast_lstm(model, batch_size, X): X = X.reshape(1, 1, len(X)) yhat = model.predict(X, batch_size=batch_size) return yhat[0, 0] # 加载数据 series = read_csv('data_set/shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser) # 让数据变成稳定的 raw_values = series.values diff_values = difference(raw_values, 1)#转换成差分数据 # 把稳定的数据变成有监督数据 supervised = timeseries_to_supervised(diff_values, 1) supervised_values = supervised.values # 数据拆分:训练数据、测试数据,前24行是训练集,后12行是测试集 train, test = supervised_values[0:-12], supervised_values[-12:] # 数据缩放 scaler, train_scaled, test_scaled = scale(train, test) #重复实验 repeats = 30 error_scores = list() for r in range(repeats): # fit 模型 lstm_model = fit_lstm(train_scaled, 1, 100, 4) # 训练数据,batch_size,epoche次数, 神经元个数 # 预测 train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)#训练数据集转换为可输入的矩阵 lstm_model.predict(train_reshaped, batch_size=1)#用模型对训练数据矩阵进行预测 # 测试数据的前向验证,实验发现,如果训练次数很少的话,模型回简单的把数据后移,以昨天的数据作为今天的预测值,当训练次数足够多的时候 # 才会体现出来训练结果 predictions = list() for i in range(len(test_scaled)): # 1步长预测 X, y = test_scaled[i, 0:-1], test_scaled[i, -1] yhat = forcast_lstm(lstm_model, 1, X) # 逆缩放 yhat = invert_scale(scaler, X, yhat) # 逆差分 yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i) predictions.append(yhat) expected = raw_values[len(train) + i + 1] print('Moth=%d, Predicted=%f, Expected=%f' % (i + 1, yhat, expected)) # 性能报告 rmse = sqrt(mean_squared_error(raw_values[-12:], predictions)) print('%d) Test RMSE:%.3f' %(r+1,rmse)) error_scores.append(rmse) #统计信息 results = DataFrame() results['rmse'] = error_scores print(results.describe()) results.boxplot() pyplot.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
机器学习 深度学习
版权声明:本文内容由网络用户投稿,版权归原作者所有,本站不拥有其著作权,亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容,请联系我们jiasou666@gmail.com 处理,核实后本网站将在24小时内删除侵权内容。