Python - 如何重塑两个向量并将其转换为元组？

Question

I have a problem. I get a task.

Create LinearRegression X to Y.

fit() a to reshape X and Y vectors new shape: (-1, 1).

This is part of my code

tuple1 = tuple(zip(X,Y))
np.reshape(tuple1, (-1, 1))
reg = LinearRegression().fit(tuple1)

I don't understand the question. The problem is the three last lines in my code. So first I should merge X and Y into a tuple to make reshape? But then I must use linear regression so I need X and Y which are not merged. I don't get it.

Answer 1

As the method fit() accepts properly shaped arrays, ...

The way it is defined, X is a 1D vector ( X.shape gives (5,) )

as scikit-learn fit() methods in general expect an array of vectors

So X is a problem, because that's not an array of vectors, but just a 1D vector.

reshape X and Y vectors by using the method reshape() and passing to it a tuple with a new shape: (-1, 1)

X.reshape(-1, 1).shape gives (5, 1) , which is what we need. I see where you got confused: The "tuple" refers to the arguments of the reshape function (literally the tuple (-1, 1) ), not to the result of the transformation.

Perform the reshaping on site (in the function call), keep the original vectors as they are.

Reshape in the function call: reg = LinearRegression().fit(X.reshape(-1, 1), Y) , ie don't mess with the variables beforehand.

Note: X can stay the way it is, because that's ok as a 1D vector (only one dependent variable); so "you will have to reshape X and Y vectors" is not correct.