Need help implementing K means clustering from scratch in Python (without using sklearn such as sklearn.neighbors.KMeans)

How to fill in the fit and predict part?

if __name__ == '__main__': from sklearn.metrics import mean_squared_error import numpy as np from sklearn.datasets import load_iris dataset = load_iris() K = 3 k = KMeansClus(K) k.fit(dataset.data) predict = k.predict(dataset.data) for k in range(K): i = np.where(predict == k) features = dataset.data[i] MSE = mean_squared_error(np.tile(k.cluster_centers_[k], (features.shape[0], 1)),features) print('Cluster', k, 'MSE', MSE) assert(MSE < 0.2) class KMeansClus: def __init__(self, K): self.K = K self.clustercenters_ = None

def fit(self, X): pass

def predict(self, X): pass