from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, RandomForestClassifier, BaggingClassifier from util import BaggedTreeRegressor, BaggedTreeClassifier # make simple regression data # N = 15 # D = 100 # X = (np.random.random((N, D)) - 0.5)*10 # Y = X.sum(axis=1)**2 + 0.5*np.random.randn(N) # Ntrain = N/2 # Xtrain = X[:Ntrain] # Ytrain = Y[:Ntrain] # Xtest = X[Ntrain:] # Ytest = Y[Ntrain:] from rf_classification import get_data X, Y = get_data() Ntrain = int(0.8*len(X)) Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] Xtest, Ytest = X[Ntrain:], Y[Ntrain:] # from rf_regression import get_data # Xtrain, Ytrain, Xtest, Ytest = get_data() class NotAsRandomForest: def __init__(self, n_estimators): self.B = n_estimators def fit(self, X, Y, M=None): N, D = X.shape if M is None: M = int(np.sqrt(D))