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June 26, 2019 06:58
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ML cheat sheet
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# Easy-to-use Python package for basic machine learning | |
# scikit-learn: https://scikit-learn.org/stable/ | |
# All the following examples are based on it. | |
# 1. Principle Component Analysis | |
# https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html | |
import numpy as np | |
from sklearn.decomposition import IncrementalPCA | |
# X is your data, it can be an n * d array, where n is the number of data, | |
# and d is the number of features (columns) of each data row. | |
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) | |
# You can set n_components to arbitrary number, normally it should be smaller | |
# than d. Here I set it to 2 since the sample data X has d = 2. | |
n_components = 2 | |
pca = IncrementalPCA(n_components=2, batch_size=3) | |
pca.fit(X) | |
ipca.transform(X) | |
# ============================================================================== | |
# 2. Random Forrests | |
from sklearn.ensemble import RandomForestRegressor | |
# this is just for generating data | |
from sklearn.datasets import make_regression | |
# sample data | |
X, y = make_regression(n_features=4, n_informative=2, random_state=0, shuffle=False) | |
regr = RandomForestRegressor(max_depth=2, random_state=0, n_estimators=100) | |
# run regression | |
regr.fit(X, y) | |
# make prediction on a sample point (0,0,0,0) | |
print(regr.predict([[0, 0, 0, 0]])) |
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