Question

是否可以从Sklean库中添加预测功能？以及如何做到？

def monomial(a,b):
    return lambda x : a * math.pow(x,b)

返回构成所需顺序的多项式的单项式列表

def polyList(order):
    return [monomial(1,i) for i in range(0,order+1)]

返回给定输入的功能总和

def evaluate(functionList, x):
    return sum([f(x) for f in functionList])

返回加权和，即w0f0 + w1f1 + ...

def weightedSum(w,F):
    if(len(w) != len(F)):
        raise Exception("Function/weight size mismatch")
    else:
        return lambda x:sum([w[i]*F[i](x) for i in range(0,len(w))])
############

在这里，我们将给定阶数的多项式与权重的最大似然估计拟合。

def polyTrain(x,y,order): 
    #Initialize the weight vector and design matrix
    w = [1 for i in range(0,order)]
    F = polyList(order)
    design = [[f(i) for f in F] for i in x]
    #Convert them to numpy arrays
    w = numpy.asarray(w)
    design = numpy.asarray(design)
    #We solve Ax=b, [x values x 3][coefficients]T = [yvalues]
    pinv = numpy.linalg.pinv(design)
    t = numpy.asarray(y).T
    #We know that the ML estimates for w are w* = pinv(design)y.T
    w = numpy.dot(pinv,t)
    return weightedSum(w,F)

Answer 1

最好定义一个可以处理所有逻辑的类。不过，如果你想要编写完全符合scikit-learn中使用的fit-transform-predict协议的代码，您需要从某个基类继承 scikit学习类，例如BaseEstimator，TransformerMixin，BaseRegressor。

Numpy提供了非常方便的功能vander，可以极大地帮助您当您使用多项式时。

让我们定义一个类。

class PolyRegressor:  # I omit subclassing for now. 

    def __init__(self, weights=None):
        self.weights = np.array(weights) if weights is not None else None

    @property
    def order(self):
        return len(self.weights) if self.weights is not None else 0

    def evaluate(self, x):
        return np.dot(np.vander(x, self.order), self.weights[:, np.newaxis]).ravel()

    def fit(self, X, y=None):
        self.weights = (np.linalg.pinv(np.vander(X, self.order)) @ y[:, np.newaxis]).ravel()

    def predict(self, X):
        if self.weights is not None: 
            return self.evaluate(X)
        else:
            raise Exception("Model wasn't fitted. Fit model first. ")

    def fit_predict(self, X, y=None):
        self.fit(X, y)
        return self.predict(X)


reg = PolyRegressor()

reg.weights = np.array([1,2,3])  # we implicitly define order = 2 here, e.g. 3 + 2x + 1x^2

reg.evaluate(np.array([5])) # testing

array（[38]）＃输出

reg.fit_predict(np.random.rand(10), np.random.rand(10) * 5)

array（[2.55922997，1.81433623，2.29153779，1.78458414，1.75961514， 2.59770317、2.65122647、1.81313616、2.61993941、2.63325695]）

根据您的需要采用代码。希望有帮助...

如何在多项式回归中添加预测

1 个答案: