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 30 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 30 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 30 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 30 mins ago
kaskas
477
asked 30 mins ago
kaskas
477
asked 30 mins ago
kaskas
477
asked 30 mins ago
kaskas
477
asked 30 mins ago
kaskas
477
477
add a comment |
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