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1

$begingroup$

I have a function in which I want to represent my data like this:

Input is a column:[ 123 125 11 122 ...]

Output:  123  125

         125  11

         11   122

The function in python is like that:

def create_dataset(dataset, look_back=1):

dataX, dataY = [], []

for i in range(len(dataset)-look_back-1):

    a = dataset[i:(i+look_back), 0]

    dataX.append(a)

    dataY.append(dataset[i + look_back, 0])

return np.array(dataX), np.array(dataY)

My dataset is represented as 1-grams of integers in a csv file, so I was obliged to use the transpose () of the dataframe to use this function. The problem is that I find the dataframe size null, and the train and test data (after spliting) also empty.

the code is:

dataframe = pd.read_csv("train2.csv")



print(dataframe.shape)  # (0,150)

print("--------n")

#dataframe= np.asarray(dataframe)

dataframe = dataframe.transpose()

print(dataframe.shape)   #(150,0)

print(dataframe.size)  # 0



dataset = dataframe.values

dataset = dataset.astype('float32')



# split into train and test sets

train_size = int(len(dataset) * 0.67)

print(len(dataset))

#print (train_size)

test_size = len(dataset) - train_size

train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

print(len(train), len(test))



print(dataset[0:train_size,:])  #[]

print(train)                    # []

print(test)                     # []



# reshape into X=t and Y=t+1

look_back = 1

trainX, trainY = create_dataset(train, look_back)

testX, testY = create_dataset(test, look_back)

Any solution please?

python keras

share|improve this question

asked 10 hours ago

KikioKikio

324

$endgroup$

add a comment | 

1

$begingroup$

I have a function in which I want to represent my data like this:

Input is a column:[ 123 125 11 122 ...]

Output:  123  125

         125  11

         11   122

The function in python is like that:

def create_dataset(dataset, look_back=1):

dataX, dataY = [], []

for i in range(len(dataset)-look_back-1):

    a = dataset[i:(i+look_back), 0]

    dataX.append(a)

    dataY.append(dataset[i + look_back, 0])

return np.array(dataX), np.array(dataY)

My dataset is represented as 1-grams of integers in a csv file, so I was obliged to use the transpose () of the dataframe to use this function. The problem is that I find the dataframe size null, and the train and test data (after spliting) also empty.

the code is:

dataframe = pd.read_csv("train2.csv")



print(dataframe.shape)  # (0,150)

print("--------n")

#dataframe= np.asarray(dataframe)

dataframe = dataframe.transpose()

print(dataframe.shape)   #(150,0)

print(dataframe.size)  # 0



dataset = dataframe.values

dataset = dataset.astype('float32')



# split into train and test sets

train_size = int(len(dataset) * 0.67)

print(len(dataset))

#print (train_size)

test_size = len(dataset) - train_size

train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

print(len(train), len(test))



print(dataset[0:train_size,:])  #[]

print(train)                    # []

print(test)                     # []



# reshape into X=t and Y=t+1

look_back = 1

trainX, trainY = create_dataset(train, look_back)

testX, testY = create_dataset(test, look_back)

Any solution please?

python keras

share|improve this question

asked 10 hours ago

KikioKikio

324

$endgroup$

add a comment | 

$begingroup$

I have a function in which I want to represent my data like this:

Input is a column:[ 123 125 11 122 ...]

Output:  123  125

         125  11

         11   122

The function in python is like that:

def create_dataset(dataset, look_back=1):

dataX, dataY = [], []

for i in range(len(dataset)-look_back-1):

    a = dataset[i:(i+look_back), 0]

    dataX.append(a)

    dataY.append(dataset[i + look_back, 0])

return np.array(dataX), np.array(dataY)

My dataset is represented as 1-grams of integers in a csv file, so I was obliged to use the transpose () of the dataframe to use this function. The problem is that I find the dataframe size null, and the train and test data (after spliting) also empty.

the code is:

dataframe = pd.read_csv("train2.csv")



print(dataframe.shape)  # (0,150)

print("--------n")

#dataframe= np.asarray(dataframe)

dataframe = dataframe.transpose()

print(dataframe.shape)   #(150,0)

print(dataframe.size)  # 0



dataset = dataframe.values

dataset = dataset.astype('float32')



# split into train and test sets

train_size = int(len(dataset) * 0.67)

print(len(dataset))

#print (train_size)

test_size = len(dataset) - train_size

train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

print(len(train), len(test))



print(dataset[0:train_size,:])  #[]

print(train)                    # []

print(test)                     # []



# reshape into X=t and Y=t+1

look_back = 1

trainX, trainY = create_dataset(train, look_back)

testX, testY = create_dataset(test, look_back)

Any solution please?

python keras

share|improve this question

asked 10 hours ago

KikioKikio

324

$endgroup$

Input is a column:[ 123 125 11 122 ...]

Output:  123  125

         125  11

         11   122

def create_dataset(dataset, look_back=1):

dataX, dataY = [], []

for i in range(len(dataset)-look_back-1):

    a = dataset[i:(i+look_back), 0]

    dataX.append(a)

    dataY.append(dataset[i + look_back, 0])

return np.array(dataX), np.array(dataY)

dataframe = pd.read_csv("train2.csv")



print(dataframe.shape)  # (0,150)

print("--------n")

#dataframe= np.asarray(dataframe)

dataframe = dataframe.transpose()

print(dataframe.shape)   #(150,0)

print(dataframe.size)  # 0



dataset = dataframe.values

dataset = dataset.astype('float32')



# split into train and test sets

train_size = int(len(dataset) * 0.67)

print(len(dataset))

#print (train_size)

test_size = len(dataset) - train_size

train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

print(len(train), len(test))



print(dataset[0:train_size,:])  #[]

print(train)                    # []

print(test)                     # []



# reshape into X=t and Y=t+1

look_back = 1

trainX, trainY = create_dataset(train, look_back)

testX, testY = create_dataset(test, look_back)

share|improve this question

asked 10 hours ago

KikioKikio

324

share|improve this question

asked 10 hours ago

KikioKikio

324

asked 10 hours ago

KikioKikio

324

asked 10 hours ago

KikioKikio

324