我有很多样本,其中假设y
在a,b,c
中作为多项式变化到一定程度。例如,对于给定的数据集和次数2,我可能会生成模型
y = a^2 + 2ab - 3cb + c^2 +.5ac
这可以使用最小二乘来完成,并且是numpy的polyfit例程的一个轻微扩展。在Python生态系统中有没有标准实现?
发布于 2015-07-17 10:23:38
sklearn提供了一种简单的方法来实现这一点。
根据发布的here构建示例
#X is the independent variable (bivariate in this case)
X = array([[0.44, 0.68], [0.99, 0.23]])
#vector is the dependent data
vector = [109.85, 155.72]
#predict is an independent variable for which we'd like to predict the value
predict= [0.49, 0.18]
#generate a model of polynomial features
poly = PolynomialFeatures(degree=2)
#transform the x data for proper fitting (for single variable type it returns,[1,x,x**2])
X_ = poly.fit_transform(X)
#transform the prediction to fit the model type
predict_ = poly.fit_transform(predict)
#here we can remove polynomial orders we don't want
#for instance I'm removing the `x` component
X_ = np.delete(X_,(1),axis=1)
predict_ = np.delete(predict_,(1),axis=1)
#generate the regression object
clf = linear_model.LinearRegression()
#preform the actual regression
clf.fit(X_, vector)
print("X_ = ",X_)
print("predict_ = ",predict_)
print("Prediction = ",clf.predict(predict_))
下面是输出:
>>> X_ = [[ 0.44 0.68 0.1936 0.2992 0.4624]
>>> [ 0.99 0.23 0.9801 0.2277 0.0529]]
>>> predict_ = [[ 0.49 0.18 0.2401 0.0882 0.0324]]
>>> Prediction = [ 126.84247142]
发布于 2016-09-30 23:09:23
sklearn有一个使用他们的管道here的很好的例子。下面是他们示例的核心:
polynomial_features = PolynomialFeatures(degree=degrees[i],
include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("polynomial_features", polynomial_features),
("linear_regression", linear_regression)])
pipeline.fit(X[:, np.newaxis], y)
您不需要自己转换数据--只需将其传递到管道中即可。
https://stackoverflow.com/questions/10988082
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