import numpy as np
from scipy.stats import mode
from scipy.spatial.distance import cdist
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import RadiusNeighborsClassifier as skRadiusNeighborsClassifier

class RadiusNeighborsClassifier():
    def __init__(self, radius=1.0):
        self.radius = radius

    def fit(self, X, y):
        self._fit_X = X
        self.classes_, self._y = np.unique(y, return_inverse=True)
        return self

    def predict(self, X):
        dist_mat = cdist(X, self._fit_X)
        neigh_ind = [np.where(d <= self.radius)[0] for d in dist_mat]
        ind = np.array([mode(self._y[n])[0] for n in neigh_ind]).ravel()
        return self.classes_[ind]

X, y = load_breast_cancer(return_X_y=True)
X = StandardScaler().fit_transform(X)
clf1 = RadiusNeighborsClassifier(radius=5).fit(X, y)
clf2 = skRadiusNeighborsClassifier(radius=5).fit(X, y)
pred1 = clf1.predict(X)
pred2 = clf2.predict(X)
assert np.array_equal(pred1, pred2)