fit and predict
~10 mincode completion
Write fit_and_predict(x_train, y_train, x_new) that fits a on one feature and returns its predictions for x_new.
All three arrive as flat 1-D arrays, so you will need to reshape the two x arrays.
Examples
A perfect doubling relationship is learned exactly
- Input
- fit_and_predict([1, 2, 3, 4], [2, 4, 6, 8], [5])
- Output
- [10]
A line with an offset is learned, intercept included
- Input
- fit_and_predict([0, 1, 2], [1, 3, 5], [3, 4])
- Output
- [7, 9]
Hints
Hint 1
Reshape so the two arrays broadcast against each other.
Hint 2
Watch for this: passed 1d X to fit.
Requirements
x_train: 1-D array of training inputsy_train: 1-D array of training targetsx_new: 1-D array of inputs to predict forReturn a 1-D array of predictions for x_new.
Constraints
Allowed library: NumPy only
Time limit: 200 ms, Memory: 64 MB
Where this shows up
~10 min
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Python
import numpy as np
from sklearn.linear_model import LinearRegression
def fit_and_predict(x_train, y_train, x_new):
"""
Fit a one-feature linear model and predict.
Args:
x_train: 1-D array of training inputs
y_train: 1-D array of training targets
x_new: 1-D array of inputs to predict for
Returns:
A 1-D array of predictions for x_new.
"""
# YOUR CODE HERE
pass