import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
#X is the independent variable (bivariate in this case)
X = np.array([[0.44, 0.68], [0.99, 0.23]])
#vector is the dependent data
vector = np.array([109.85, 155.72])
#predict is an independent variable for which we'd like to predict the value
predict= np.array([[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_))
def model(p, v, x, w):
a,b,c,d,e,f,g,h,i,j,k = p #coefficients to the polynomials
return a*v**2 + b*x**2 + c*w**2 + d*v*x + e*v*w + f*x*w + g*v + h*x + i*y + k
def residuals(p, data): # Function needed by fit routine
v, x, w, z = data # The values for v, x, w and the measured hypersurface z
a,b,c,d,e,f,g,h,i,j,k = p #coefficients to the polynomials
return (z-model(p,v,x,w)) # Returns an array of residuals.
#This should (z-model(p,v,x,w))/err if
# there are error bars on the measured z values
#initial guess at parameters. Avoid using 0.0 as initial guess
par0 = [1.0, 1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]
#create a fitting object. data should be in the form
#that the functions above are looking for, i.e. a Nx4
#list of lists/tuples like (v,x,w,z)
fitobj = kmpfit.Fitter(residuals=residuals, data=data)
# call the fitter
fitobj.fit(params0=par0)
3条答案
按热度按时间nhaq1z211#
sklearn提供了一个简单的方法来做到这一点。
以here为例:
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输出如下:
型
vsmadaxz2#
polyfit确实有效,但有更好的最小二乘最小化器。
http://www.astro.rug.nl/software/kapteyn-beta/kmpfittutorial.html
它比polyfit更强大,在他们的页面上有一个例子,展示了如何进行简单的线性拟合,应该提供进行二阶多项式拟合的基础知识。
个
这些事情的成功与拟合的初始值密切相关,因此如果可能的话,请仔细选择。由于有如此多的自由参数,因此获得解决方案可能是一个挑战。
92dk7w1h3#
sklearn有一个很好的例子,使用了他们的Pipeline here。下面是他们例子的核心:
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您不需要自己转换数据-只需将其传递到Pipeline中。