How to do add and subtraction in between three inputs for predict the value using python
0
$begingroup$
This question is related to this unsupported operand type(s) for -: 'list' and 'list' using python
I want to predict value according to the three inputs(X1,X2,X3) . for prediction value,
three inputs
X1-X2+X3 = predict value
according to this algorithm value will be predicted using LSTM neural network. I wrote the code but it gives me so many errors. Can anyone suggest me to solve this error?
here is my code:
data.columns = ['X1', 'X2', 'X3','Y']
data = data.dropna ()
y =data['Y'].astype(int)
cols=['X1', 'X2', 'X3']
x=data[cols].astype(int)
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),3 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
n = data.shape[0]
p = data.shape[1]
data = data.values
a =
for i in range(0,len(data)):
X1 = data[i][0]
a.append([X1])
b =
for i in range(0,len(data)):
X2 = data[i][1]
b.append([X2])
c =
for i in range(0,len(data)):
X3 = data[i][2]
c.append([X3])
train_start = 0
train_end = int(np.floor(0.8*n))
test_start = train_end+1
test_end = n
x_train = x[np.arange(train_start, train_end), :]
x_test = x[np.arange(test_start, test_end), :]
y_train = y[np.arange(train_start, train_end), :]
y_test = y[np.arange(test_start, test_end), :]
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
for i in range(len(x_train)):
x_train.append([a[i] ,b[i], c[i]])
x.append((a[i][0] - b[i][0] + c[i][0]))
x_train =np.array(x_train)
x = np.array(x)
seed = 20
np.random.seed(seed)
fit1 = Sequential ()
fit1.add(LSTM(
output_dim = 5,
activation='relu',
input_shape =(3,1)))
fit1.add(Dense(output_dim =1))
fit1.add(Activation(linear))
batchsize = 1
fit1.compile(loss="mean_squared_error",optimizer="adam")
#train the model
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =1, shuffle=True)
score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
#Make prediction
pred1=fit1.predict(x_test)
#data=pd.DataFrame(fit1.predict(x_test))
pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test)))
Here is my csv file;
python lstm
asked 8 mins ago
kaskas
477
$endgroup$
add a comment |
0
$begingroup$
This question is related to this unsupported operand type(s) for -: 'list' and 'list' using python
I want to predict value according to the three inputs(X1,X2,X3) . for prediction value,
three inputs
X1-X2+X3 = predict value
according to this algorithm value will be predicted using LSTM neural network. I wrote the code but it gives me so many errors. Can anyone suggest me to solve this error?
here is my code:
data.columns = ['X1', 'X2', 'X3','Y']
data = data.dropna ()
y =data['Y'].astype(int)
cols=['X1', 'X2', 'X3']
x=data[cols].astype(int)
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),3 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
n = data.shape[0]
p = data.shape[1]
data = data.values
a =
for i in range(0,len(data)):
X1 = data[i][0]
a.append([X1])
b =
for i in range(0,len(data)):
X2 = data[i][1]
b.append([X2])
c =
for i in range(0,len(data)):
X3 = data[i][2]
c.append([X3])
train_start = 0
train_end = int(np.floor(0.8*n))
test_start = train_end+1
test_end = n
x_train = x[np.arange(train_start, train_end), :]
x_test = x[np.arange(test_start, test_end), :]
y_train = y[np.arange(train_start, train_end), :]
y_test = y[np.arange(test_start, test_end), :]
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
for i in range(len(x_train)):
x_train.append([a[i] ,b[i], c[i]])
x.append((a[i][0] - b[i][0] + c[i][0]))
x_train =np.array(x_train)
x = np.array(x)
seed = 20
np.random.seed(seed)
fit1 = Sequential ()
fit1.add(LSTM(
output_dim = 5,
activation='relu',
input_shape =(3,1)))
fit1.add(Dense(output_dim =1))
fit1.add(Activation(linear))
batchsize = 1
fit1.compile(loss="mean_squared_error",optimizer="adam")
#train the model
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =1, shuffle=True)
score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
#Make prediction
pred1=fit1.predict(x_test)
#data=pd.DataFrame(fit1.predict(x_test))
pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test)))
Here is my csv file;
python lstm
asked 8 mins ago
kaskas
477
$endgroup$
add a comment |
0
0
0
$begingroup$
This question is related to this unsupported operand type(s) for -: 'list' and 'list' using python
I want to predict value according to the three inputs(X1,X2,X3) . for prediction value,
three inputs
X1-X2+X3 = predict value
according to this algorithm value will be predicted using LSTM neural network. I wrote the code but it gives me so many errors. Can anyone suggest me to solve this error?
here is my code:
data.columns = ['X1', 'X2', 'X3','Y']
data = data.dropna ()
y =data['Y'].astype(int)
cols=['X1', 'X2', 'X3']
x=data[cols].astype(int)
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),3 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
n = data.shape[0]
p = data.shape[1]
data = data.values
a =
for i in range(0,len(data)):
X1 = data[i][0]
a.append([X1])
b =
for i in range(0,len(data)):
X2 = data[i][1]
b.append([X2])
c =
for i in range(0,len(data)):
X3 = data[i][2]
c.append([X3])
train_start = 0
train_end = int(np.floor(0.8*n))
test_start = train_end+1
test_end = n
x_train = x[np.arange(train_start, train_end), :]
x_test = x[np.arange(test_start, test_end), :]
y_train = y[np.arange(train_start, train_end), :]
y_test = y[np.arange(test_start, test_end), :]
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
for i in range(len(x_train)):
x_train.append([a[i] ,b[i], c[i]])
x.append((a[i][0] - b[i][0] + c[i][0]))
x_train =np.array(x_train)
x = np.array(x)
seed = 20
np.random.seed(seed)
fit1 = Sequential ()
fit1.add(LSTM(
output_dim = 5,
activation='relu',
input_shape =(3,1)))
fit1.add(Dense(output_dim =1))
fit1.add(Activation(linear))
batchsize = 1
fit1.compile(loss="mean_squared_error",optimizer="adam")
#train the model
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =1, shuffle=True)
score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
#Make prediction
pred1=fit1.predict(x_test)
#data=pd.DataFrame(fit1.predict(x_test))
pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test)))
Here is my csv file;
python lstm
asked 8 mins ago
kaskas
477
$endgroup$
This question is related to this unsupported operand type(s) for -: 'list' and 'list' using python
I want to predict value according to the three inputs(X1,X2,X3) . for prediction value,
three inputs
X1-X2+X3 = predict value
according to this algorithm value will be predicted using LSTM neural network. I wrote the code but it gives me so many errors. Can anyone suggest me to solve this error?
here is my code:
data.columns = ['X1', 'X2', 'X3','Y']
data = data.dropna ()
y =data['Y'].astype(int)
cols=['X1', 'X2', 'X3']
x=data[cols].astype(int)
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),3 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
n = data.shape[0]
p = data.shape[1]
data = data.values
a =
for i in range(0,len(data)):
X1 = data[i][0]
a.append([X1])
b =
for i in range(0,len(data)):
X2 = data[i][1]
b.append([X2])
c =
for i in range(0,len(data)):
X3 = data[i][2]
c.append([X3])
train_start = 0
train_end = int(np.floor(0.8*n))
test_start = train_end+1
test_end = n
x_train = x[np.arange(train_start, train_end), :]
x_test = x[np.arange(test_start, test_end), :]
y_train = y[np.arange(train_start, train_end), :]
y_test = y[np.arange(test_start, test_end), :]
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
for i in range(len(x_train)):
x_train.append([a[i] ,b[i], c[i]])
x.append((a[i][0] - b[i][0] + c[i][0]))
x_train =np.array(x_train)
x = np.array(x)
seed = 20
np.random.seed(seed)
fit1 = Sequential ()
fit1.add(LSTM(
output_dim = 5,
activation='relu',
input_shape =(3,1)))
fit1.add(Dense(output_dim =1))
fit1.add(Activation(linear))
batchsize = 1
fit1.compile(loss="mean_squared_error",optimizer="adam")
#train the model
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =1, shuffle=True)
score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
#Make prediction
pred1=fit1.predict(x_test)
#data=pd.DataFrame(fit1.predict(x_test))
pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test)))
Here is my csv file;
python lstm
python lstm
asked 8 mins ago
kaskas
477
asked 8 mins ago
kaskas
477
asked 8 mins ago
kaskas
477
asked 8 mins ago
kaskas
477
asked 8 mins ago
kaskas
477
477
add a comment |
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