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Extract array from file using sklearn

Here is my code:

import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline

X=np.array([[1, 2, 4]]).T
print(X)
y=np.array([1, 4, 16])
print(y)
model = make_pipeline(PolynomialFeatures(degree=2),
LinearRegression(fit_intercept = False))
model.fit(X,y)
X_predict = np.array([[3]])
print(model.predict(X_predict))


Plese, i'd like to extract X and y from a file like this:

x | y
1 | 1
2 | 4
4 | 16


(This is an example. My file contains more than 100 ligne).
What method i have to use ?
Kind regards.

X=np.array([[1, 2, 4]]).T

X=np.array([1, 2, 4]).T

1 Answer
1

with open('input.txt') as fp:
for line in fp:
b = line.split("|")
x,y = b


In this code x is the integer before | and y is integer after |.

|

|

So total code would be:

import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
X_arr =
Y_arr =
with open('input.txt') as fp:
for line in fp:
b = line.split("|")
x,y = b
X_arr.append(int(x))
Y_arr.append(int(y))


X=np.array([X_arr]).T
print(X)
y=np.array(Y_arr)
print(y)
model = make_pipeline(PolynomialFeatures(degree=2),
LinearRegression(fit_intercept = False))
model.fit(X,y)
X_predict = np.array([[3]])
print(model.predict(X_predict))


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