safijari
/ numba_absolute_minimum.ipynb
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January 15, 2024 08:09
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tomquisel
/ prefit_voting_classifier.py
Last active
August 29, 2024 17:40
Version of scikit-learn's VotingClassifier that uses prefit models rather than requiring a refit.
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| class VotingClassifier(object): | |
| """Stripped-down version of VotingClassifier that uses prefit estimators""" | |
| def __init__(self, estimators, voting='hard', weights=None): | |
| self.estimators = [e[1] for e in estimators] | |
| self.named_estimators = dict(estimators) | |
| self.voting = voting | |
| self.weights = weights | |
| def fit(self, X, y, sample_weight=None): | |
| raise NotImplementedError |
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| import numpy as np | |
| import pylab as pl | |
| from numpy import fft | |
| def fourierExtrapolation(x, n_predict): | |
| n = x.size | |
| n_harm = 10 # number of harmonics in model | |
| t = np.arange(0, n) | |
| p = np.polyfit(t, x, 1) # find linear trend in x | |
| x_notrend = x - p[0] * t # detrended x |