modify

mix/mix_predict_518.py

@@ -0,0 +1,102 @@
+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import load_model
+import joblib
+
+
+def read_data(path):
+    lines = []
+    with open(path) as f:
+        for line in f.readlines()[:]:
+            line = eval(line.strip())
+            lines.append(line)
+
+    size = len(lines[0])
+    train_x=[s[:size - 2] for s in lines]
+    train_y=[s[size-1] for s in lines]
+    return np.array(train_x, dtype=np.float32),np.array(train_y, dtype=np.float32),lines
+
+
+def _score(fact, line):
+    up_right = 0
+    up_error = 0
+
+    if fact[0] == 1:
+        up_right = up_right + 1.1
+    elif fact[1] == 1:
+        up_error = up_error + 0.2
+        up_right = up_right + 1.04
+    elif fact[2] == 1:
+        up_error = up_error + 0.5
+        up_right = up_right + 0.98
+    else:
+        up_error = up_error + 1
+        up_right = up_right + 0.94
+    return up_right,up_error
+
+
+def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1, row=18, col=20):
+    test_x,test_y,lines=read_data(file_path)
+
+    test_x_a = test_x[:,:row*col]
+    test_x_a = test_x_a.reshape(test_x.shape[0], row, col, 1)
+    # test_x_b = test_x[:, row*col:row*col+18*2]
+    # test_x_b = test_x_b.reshape(test_x.shape[0], 18, 2, 1)
+    test_x_c = test_x[:,row*col:]
+
+    model=load_model(model_path)
+    score = model.evaluate([test_x_c, test_x_a, ], test_y)
+    print('MIX', score)
+
+    up_num = 0
+    up_error = 0
+    up_right = 0
+    down_num = 0
+    down_error = 0
+    down_right = 0
+    i = 0
+    result = model.predict([test_x_c, test_x_a, ])
+    win_dnn = []
+    for r in result:
+        fact = test_y[i]
+
+        if idx in [-2]:
+            if r[0] > 0.5 or r[1] > 0.5:
+                pass
+        else:
+            if r[0] > 0.5 :
+                tmp_right,tmp_error = _score(fact, lines[i])
+                up_right = tmp_right + up_right
+                up_error = tmp_error + up_error
+                up_num = up_num + 1
+            elif r[2] > 0.5 or r[3] > 0.5:
+                if fact[0] == 1:
+                    down_error = down_error + 1
+                    down_right = down_right + 1.1
+                elif fact[1] == 1:
+                    down_error = down_error + 0.5
+                    down_right = down_right + 1.04
+                elif fact[2] == 1:
+                    down_right = down_right + 0.98
+                else:
+                    down_right = down_right + 0.92
+                down_num = down_num + 1
+
+        i = i + 1
+    if up_num == 0:
+        up_num = 1
+    if down_num == 0:
+        down_num = 1
+    print('MIX', up_right, up_num, up_right/up_num, up_error/up_num, down_right/down_num, down_error/down_num)
+    return win_dnn,up_right/up_num,down_right/down_num
+
+
+if __name__ == '__main__':
+    # predict(file_path='D:\\data\\quantization\\stock181_18d_test.log', model_path='181_18d_mix_6D_ma5_s_seq.h5')
+    # predict(file_path='D:\\data\\quantization\\stock217_18d_train1.log', model_path='218_18d_mix_5D_ma5_s_seq.h5', row=18, col=18)
+    # predict(file_path='D:\\data\\quantization\\stock400_18d_train1.log', model_path='400_18d_mix_5D_ma5_s_seq.h5', row=18, col=18)
+    predict(file_path='D:\\data\\quantization\\stock517_28d_train1.log', model_path='517_28d_mix_3D_ma5_s_seq.h5', row=28, col=16)
+    # predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
+    # multi_predict(model='15_18d')
+    # predict_today(20200229, model='11_18d')
+    # predict(file_path='D:\\data\\quantization\\stock513_28d_train1.log', model_path='513_28d_mix_3D_ma5_s_seq.h5', row=28, col=16)

mix/mix_train_518.py

@@ -0,0 +1,212 @@
+import keras
+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import Sequential
+# 优化方法选用Adam(其实可选项有很多，如SGD)
+from keras.optimizers import Adam
+import random
+from keras.models import load_model
+from imblearn.over_sampling import RandomOverSampler
+from keras.utils import np_utils
+# 用于模型初始化，Conv2D模型初始化、Activation激活函数，MaxPooling2D是池化层
+# Flatten作用是将多位输入进行一维化
+# Dense是全连接层
+from keras.layers import Conv2D, Activation, MaxPool2D, Flatten, Dense,Dropout,Input,MaxPooling2D,BatchNormalization,concatenate
+from keras import regularizers
+from keras.models import Model
+from keras.callbacks import EarlyStopping
+
+early_stopping = EarlyStopping(monitor='accuracy', patience=5, verbose=2)
+
+epochs= 30
+size = 470000 #共68W
+file_path = 'D:\\data\\quantization\\stock517_28d_train2.log'
+model_path = '517_28d_mix_3D_ma5_s_seq.h5'
+file_path1='D:\\data\\quantization\\stock517_28d_test.log'
+row = 28
+col = 16
+'''
+0    dmi            28*20      38,98,51/5     下跌预判非常准                                    
+1    macd           28*19      41,98,53/8  
+2    dmi-对大盘对比 28*20      35,99,46/17 
+3    5d-dmi-对大盘对比 28*20   42,99,39/10
+4    3d-dmi-对大盘对比 28*20   40,99,39/07   
+5    3d-beta1                  55,99,52/07    当前用这个
+6    3d-ma20                   40,99,41/07
+7    3d-macd   28*19           55,99,40/07
+8    3d-市值>30  28*20         57,101,36/14   最高价  用这个！
+9    3d-市值>30  28*20         57,99,31/08   收盘最高价 
+10   5d-市值>30  28*20   收盘最高价  
+11   5d-市值>30  28*20   ma5
+12   5d-极简     28*16 有ma5,ma20  46,102,16/26
+13   3d-最高价   28*16         57,101,39,16
+14   5d-极简-最高价     28*16   40,102,30-34
+15   5d+dmi+最高价      28*20   40,102,31-34    
+16   同12,14,参数11,10  28*16   38,102,29-36
+17   同上参数11,6       28*16   39,102,30-35  !
+
+'''
+
+def read_data(path, path1=file_path1):
+    lines = []
+    with open(path) as f:
+        for x in range(size): #680000
+            line = eval(f.readline().strip())
+            lines.append(line)
+
+    with open(path1) as f:
+        for x in range(30000): #6w
+            line = eval(f.readline().strip())
+            lines.append(line)
+
+    random.shuffle(lines)
+    print('读取数据完毕')
+
+    d=int(0.85*len(lines))
+    length = len(lines[0])
+
+    train_x=[s[:length - 2] for s in lines[0:d]]
+    train_y=[s[-1] for s in lines[0:d]]
+    test_x=[s[:length - 2] for s in lines[d:]]
+    test_y=[s[-1] for s in lines[d:]]
+
+    print('转换数据完毕')
+
+    ros = RandomOverSampler(random_state=0)
+    X_resampled, y_resampled = ros.fit_sample(np.array(train_x, dtype=np.float32), np.array(train_y, dtype=np.float32))
+
+    print('数据重采样完毕')
+
+    return X_resampled,y_resampled,np.array(test_x, dtype=np.float32),np.array(test_y, dtype=np.float32)
+
+
+train_x,train_y,test_x,test_y=read_data(file_path)
+
+train_x_a = train_x[:,:row*col]
+train_x_a = train_x_a.reshape(train_x.shape[0], row, col, 1)
+# train_x_b = train_x[:, 9*26:18*26]
+# train_x_b = train_x_b.reshape(train_x.shape[0], 9, 26, 1)
+train_x_c = train_x[:,row*col:]
+
+
+def create_mlp(dim, regress=False):
+    # define our MLP network
+    model = Sequential()
+    model.add(Dense(256, input_dim=dim, activation="relu"))
+    model.add(Dropout(0.2))
+    model.add(Dense(256, activation="relu"))
+    model.add(Dense(256, activation="relu"))
+    model.add(Dense(128, activation="relu"))
+
+    # check to see if the regression node should be added
+    if regress:
+        model.add(Dense(1, activation="linear"))
+
+    # return our model
+    return model
+
+
+def create_cnn(width, height, depth, size=48, kernel_size=(5, 6), regress=False, output=24):
+    # initialize the input shape and channel dimension, assuming
+    # TensorFlow/channels-last ordering
+    inputShape = (width, height, 1)
+    chanDim = -1
+
+    # define the model input
+    inputs = Input(shape=inputShape)
+    # x = inputs
+    # CONV => RELU => BN => POOL
+    x = Conv2D(size, kernel_size, strides=2, padding="same")(inputs)
+    x = Activation("relu")(x)
+    x = BatchNormalization(axis=chanDim)(x)
+
+    # y = Conv2D(24, (2, 8), strides=2, padding="same")(inputs)
+    # y = Activation("relu")(y)
+    # y = BatchNormalization(axis=chanDim)(y)
+
+    # flatten the volume, then FC => RELU => BN => DROPOUT
+    x = Flatten()(x)
+    x = Dense(output)(x)
+    x = Activation("relu")(x)
+    x = BatchNormalization(axis=chanDim)(x)
+    x = Dropout(0.2)(x)
+
+    # apply another FC layer, this one to match the number of nodes
+    # coming out of the MLP
+    x = Dense(output)(x)
+    x = Activation("relu")(x)
+
+    # check to see if the regression node should be added
+    if regress:
+        x = Dense(1, activation="linear")(x)
+
+    # construct the CNN
+    model = Model(inputs, x)
+
+    # return the CNN
+    return model
+
+
+# create the MLP and CNN models
+mlp = create_mlp(train_x_c.shape[1], regress=False)
+# cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 3), size=90, regress=False, output=96)       # 31 97 46
+cnn_0 = create_cnn(row, col, 1, kernel_size=(6, col), size=96, regress=False, output=96)         # 29 98 47
+# cnn_0 = create_cnn(18, 20, 1, kernel_size=(9, 9), size=90, regress=False, output=96)         # 28 97 53
+# cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 20), size=90, regress=False, output=96)
+# cnn_1 = create_cnn(18, 20, 1, kernel_size=(18, 10), size=80, regress=False, output=96)
+# cnn_1 = create_cnn(9, 26, 1, kernel_size=(2, 14), size=36, regress=False, output=64)
+
+# create the input to our final set of layers as the *output* of both
+# the MLP and CNN
+combinedInput = concatenate([mlp.output, cnn_0.output, ])
+
+# our final FC layer head will have two dense layers, the final one
+# being our regression head
+x = Dense(1024, activation="relu", kernel_regularizer=regularizers.l1(0.003))(combinedInput)
+x = Dropout(0.2)(x)
+x = Dense(1024, activation="relu")(x)
+x = Dense(1024, activation="relu")(x)
+# 在建设一层
+x = Dense(4, activation="softmax")(x)
+
+# our final model will accept categorical/numerical data on the MLP
+# input and images on the CNN input, outputting a single value (the
+# predicted price of the house)
+model = Model(inputs=[mlp.input, cnn_0.input, ], outputs=x)
+
+
+print("Starting training ")
+# h = model.fit(train_x, train_y, batch_size=4096*2, epochs=500, shuffle=True)
+
+# compile the model using mean absolute percentage error as our loss,
+# implying that we seek to minimize the absolute percentage difference
+# between our price *predictions* and the *actual prices*
+opt = Adam(lr=1e-3, decay=1e-3 / 200)
+model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=['accuracy'])
+
+# train the model
+print("[INFO] training model...")
+model.fit(
+    [train_x_c, train_x_a, ], train_y,
+    # validation_data=([testAttrX, testImagesX], testY),
+    # epochs=int(3*train_x_a.shape[0]/1300),
+    epochs=epochs,
+    batch_size=2048, shuffle=True,
+    callbacks=[early_stopping]
+)
+
+model.save(model_path)
+
+test_x_a = test_x[:,:row*col]
+test_x_a = test_x_a.reshape(test_x.shape[0], row, col, 1)
+# test_x_b = test_x[:, 9*26:9*26+9*26]
+# test_x_b = test_x_b.reshape(test_x.shape[0], 9, 26, 1)
+test_x_c = test_x[:,row*col:]
+
+# make predictions on the testing data
+print("[INFO] predicting house prices...")
+score  = model.evaluate([test_x_c, test_x_a,], test_y)
+
+print(score)
+print('Test score:', score[0])
+print('Test accuracy:', score[1])

week/predict_everyweek_100.py

@@ -0,0 +1,100 @@
+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import load_model
+import random
+from mix.stock_source import *
+import pymongo
+from util.mongodb import get_mongo_table_instance
+
+code_table = get_mongo_table_instance('tushare_code')
+k_table = get_mongo_table_instance('stock_day_k')
+stock_concept_table = get_mongo_table_instance('tushare_concept_detail')
+all_concept_code_list = list(get_mongo_table_instance('tushare_concept').find({}))
+
+
+gainian_map = {}
+hangye_map = {}
+
+
+Z_list = []  # 自选
+R_list = []  #  ROE
+O_list = []  # 其他
+
+
+def predict_today(file, day, model='10_18d', log=True):
+    industry_list = get_hot_industry(day)
+
+    lines = []
+    with open(file) as f:
+        for line in f.readlines()[:]:
+            line = eval(line.strip())
+            lines.append(line)
+
+    size = len(lines[0])
+
+    model=load_model(model)
+
+    row = 18
+    col = 11
+
+    for line in lines:
+        train_x = np.array([line[:size - 1]])
+
+        train_x_a = train_x[:,:row*col]
+        train_x_a = train_x_a.reshape(train_x.shape[0], row, col, 1)
+        train_x_b = train_x[:, row*col:row*col + 11*16]
+        train_x_b = train_x_b.reshape(train_x.shape[0], 11, 16, 1)
+        train_x_c = train_x[:,row*col + 11*16:]
+
+        result = model.predict([train_x_c, train_x_a, train_x_b])
+        # print(result, line[-1])
+        stock = code_table.find_one({'ts_code':line[-1][0]})
+
+        if result[0][0] > 0.5:
+            if line[-1][0].startswith('688'):
+                continue
+            # 去掉ST
+            if stock['name'].startswith('ST') or stock['name'].startswith('N') or stock['name'].startswith('*'):
+                continue
+
+            if stock['ts_code'] in ROE_stock_list or stock['ts_code'] in zeng_stock_list:
+                R_list.append([stock['ts_code'], stock['name']])
+
+            concept_code_list = list(stock_concept_table.find({'ts_code':stock['ts_code']}))
+            concept_detail_list = []
+
+            if len(concept_code_list) > 0:
+                for concept in concept_code_list:
+                    for c in all_concept_code_list:
+                        if c['code'] == concept['concept_code']:
+                            concept_detail_list.append(c['name'])
+
+            if log is True:
+                with open('D:\\data\\quantization\\predict\\' + str(day) + '_week_100.txt', mode='a', encoding="utf-8") as f:
+                    f.write(str(line[-1]) + ' ' + stock['name'] + ' ' + stock['sw_industry'] + ' ' + str(concept_detail_list) + ' ' + str(result[0][0]) + '\n')
+
+        elif result[0][1] > 0.5:
+            pass
+        elif result[0][2] > 0.5:
+            if stock['ts_code'] in holder_stock_list:
+                print(stock['ts_code'], stock['name'], '警告危险')
+        elif result[0][3] > 0.5:
+            if stock['ts_code'] in holder_stock_list or stock['ts_code'] in zixuan_stock_list:
+                print(stock['ts_code'], stock['name'], '赶紧卖出')
+        else:
+            pass
+
+    # print(gainian_map)
+    # print(hangye_map)
+    random.shuffle(O_list)
+    print(O_list[:3])
+
+    random.shuffle(R_list)
+    print('----ROE----')
+    print(R_list[:3])
+
+
+if __name__ == '__main__':
+    # 策略B
+    # predict_today("D:\\data\\quantization\\stock505_28d_20200416.log", 20200416, model='505_28d_mix_5D_ma5_s_seq.h5', log=True)
+    predict_today("D:\\data\\quantization\\week101_18d_20200421.log", 20200421, model='103_18d_mix_3W_s_seq.h5', log=True)

week/week_predict_100.py

@@ -1,7 +1,7 @@
 # -*- encoding:utf-8 -*-
 import numpy as np
 from keras.models import load_model
-import joblib
+import random
 
 
 def read_data(path):
@@ -17,14 +17,14 @@ def read_data(path):
     return np.array(train_x, dtype=np.float32),np.array(train_y, dtype=np.float32),lines
 
 
-def _score(fact, line):
+def _score(fact, ):
     up_right = 0
     up_error = 0
 
     if fact[0] == 1:
-        up_right = up_right + 1.15
+        up_right = up_right + 1.14
     elif fact[1] == 1:
-        up_error = up_error + 0.1
+        up_error = up_error + 0.2
         up_right = up_right + 1.08
     elif fact[2] == 1:
         up_error = up_error + 0.7
@@ -59,13 +59,14 @@ def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1, row=18, col=20)
     win_dnn = []
     for r in result:
         fact = test_y[i]
+        xx = random.uniform(0, 4)
 
         if idx in [-2]:
             if r[0] > 0.5 or r[1] > 0.5:
                 pass
         else:
             if r[0] > 0.5:
-                tmp_right,tmp_error = _score(fact, lines[i])
+                tmp_right,tmp_error = _score(fact, )
                 up_right = tmp_right + up_right
                 up_error = tmp_error + up_error
                 up_num = up_num + 1
@@ -77,6 +78,7 @@ def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1, row=18, col=20)
                     down_error = down_error + 0.5
                     down_right = down_right + 1.02
                 elif fact[2] == 1:
+                    down_error = down_error + 0.2
                     down_right = down_right + 0.98
                 else:
                     down_right = down_right + 0.95
@@ -91,5 +93,57 @@ def predict(file_path='', model_path='15min_dnn_seq.h5', idx=-1, row=18, col=20)
     return win_dnn,up_right/up_num,down_right/down_num
 
 
+def random_predict(file_path=''):
+    test_x,test_y,lines=read_data(file_path)
+    a=0
+    b=0
+    c=0
+    d=0
+    R=0
+
+    up_num = 0
+    up_error = 0
+    up_right = 0
+    down_num = 0
+    down_error = 0
+    down_right = 0
+
+    for x in test_y:
+        xx = random.uniform(0, 4)
+        if xx > 3:
+            if x[0] == 1:
+                R = R + 1
+            tmp_right,tmp_error = _score(x,)
+            up_num = up_num + 1
+            up_right = up_right + tmp_right
+            up_error = tmp_error + up_error
+        elif xx > 2:
+            if x[1] == 1:
+                R = R + 1
+        elif xx > 1:
+            if x[2] == 1:
+                R = R + 1
+        elif xx > 0:
+            if x[3] == 1:
+                R = R + 1
+
+        if x[0] == 1:
+            a = a + 1
+        elif x[1] == 1:
+            b = b + 1
+        elif x[2] == 1:
+            c = c + 1
+        else:
+            d = d + 1
+
+    if up_num == 0:
+        up_num = 1
+    if down_num == 0:
+        down_num = 1
+
+    print(R/(a + b + c +d), up_right/up_num, up_error/up_num)
+
+
 if __name__ == '__main__':
-    predict(file_path='D:\\data\\quantization\\week101_18d_test.log', model_path='103_18d_mix_3W_s_seq.h5', row=18, col=11)
+    predict(file_path='D:\\data\\quantization\\week113_18d_test.log', model_path='113_18d_mix_3W_s_seq.h5', row=18, col=6)
+    # random_predict(file_path='D:\\data\\quantization\\week109/_18d_test.log')

week/week_train_100.py

@@ -16,29 +16,43 @@ from keras.callbacks import EarlyStopping
 
 early_stopping = EarlyStopping(monitor='accuracy', patience=5, verbose=2)
 
-epochs= 55
+epochs= 38
 # size = 24000 #共68W
-file_path = 'D:\\data\\quantization\\week101_18d_train1.log'
-model_path = '105_18d_mix_3W_s_seq.h5'
-file_path1='D:\\data\\quantization\\week101_18d_train1.log'
+file_path = 'D:\\data\\quantization\\week112_18d_train1.log'
+model_path = '112_18d_mix_3W_s_seq.h5'
 row = 18
 col = 11
+col1 = 17
 '''
 0   18-3                    18*11           25,102,47-29
 1   18W预测3周后最高价+pe   18*11           37,101,44-22
 2                           18*11 + 11*16   33,101,41-30
-3   stripe=1                18*11 + 11*16   32,106,24-23
-4   stripe=1,win=3          18*11 + 11*16   35,105,27-27
+3   stripe=1,win=4-3        18*11 + 11*16   38,104,32-20  ----- 随机25,100,51-26            
+4   stripe=1,win=3          18*11 + 11*16   34,103,41-26
 5   stripe=1,win=3          18*11           
+6   用ma来衡量    
+7   简化模型                
+8   ma5-大盘相关+alpha_6       18*11 + 11*16         ------25,96,69
+9   ma5-大盘相关+alpha_44+alpha_2            51,96,68-07    
+10  ma5-大盘相关+alpha_53+alpha_18           48,97,61-06
+
+11  high-大盘相关+alpha_53+alpha_18                             35,103,39-37
+12  high-大盘相关+alpha_53+alpha_18(每日)    18*11 + 11*17      
+13  high-大盘相关+alpha_53+alpha_18-dmi      18*6 + 11*16       37,105,33-32
 '''
 
-def read_data(path, path1=file_path1):
+def read_data(path):
     lines = []
     with open(path) as f:
         for x in f.readlines()[:]: #680000
             line = eval(x.strip())
             lines.append(line)
 
+    # with open(path1) as f:
+    #     for x in f.readlines()[:]: #680000
+    #         line = eval(x.strip())
+    #         lines.append(line)
+
 
     random.shuffle(lines)
     print('读取数据完毕')
@@ -65,9 +79,9 @@ train_x,train_y,test_x,test_y=read_data(file_path)
 
 train_x_a = train_x[:,:row*col]
 train_x_a = train_x_a.reshape(train_x.shape[0], row, col, 1)
-train_x_b = train_x[:, row*col:row*col + 11*16]
-train_x_b = train_x_b.reshape(train_x.shape[0], 11, 16, 1)
-train_x_c = train_x[:,row*col + 11*16:]
+train_x_b = train_x[:, row*col:row*col + 11*col1]
+train_x_b = train_x_b.reshape(train_x.shape[0], 11, col1, 1)
+train_x_c = train_x[:,row*col + 11*col1:]
 
 
 def create_mlp(dim, regress=False):
@@ -131,10 +145,10 @@ def create_cnn(width, height, depth, size=48, kernel_size=(5, 6), regress=False,
 # create the MLP and CNN models
 mlp = create_mlp(train_x_c.shape[1], regress=False)
 # cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 3), size=90, regress=False, output=96)       # 31 97 46
-cnn_0 = create_cnn(row, col, 1, kernel_size=(3, col), size=66, regress=False, output=66)         # 29 98 47
+cnn_0 = create_cnn(row, col, 1, kernel_size=(4, col), size=66, regress=False, output=66)         # 29 98 47
 # cnn_0 = create_cnn(18, 20, 1, kernel_size=(9, 9), size=90, regress=False, output=96)         # 28 97 53
 # cnn_0 = create_cnn(18, 20, 1, kernel_size=(3, 20), size=90, regress=False, output=96)
-cnn_1 = create_cnn(11, 16, 1, kernel_size=(3, 16), size=66, regress=False, output=66)
+cnn_1 = create_cnn(11, col1, 1, kernel_size=(3, col1), size=66, regress=False, output=66)
 # cnn_1 = create_cnn(9, 26, 1, kernel_size=(2, 14), size=36, regress=False, output=64)
 
 # create the input to our final set of layers as the *output* of both
@@ -180,9 +194,9 @@ model.save(model_path)
 
 test_x_a = test_x[:,:row*col]
 test_x_a = test_x_a.reshape(test_x.shape[0], row, col, 1)
-test_x_b = test_x[:, row*col:row*col + 11*16]
-test_x_b = test_x_b.reshape(test_x.shape[0],11, 16, 1)
-test_x_c = test_x[:,row*col + 11*16:]
+test_x_b = test_x[:, row*col:row*col + 11*col1]
+test_x_b = test_x_b.reshape(test_x.shape[0],11, col1, 1)
+test_x_c = test_x[:,row*col + 11*col1:]
 
 # make predictions on the testing data
 print("[INFO] predicting house prices...")