mikkelam

import numpy as np
from sklearn.tree import DecisionTreeRegressor

class LeastSquares:
    @staticmethod
    def negative_gradient(preds, y):
        return y - preds

class GradientBoostingRegressor:
    models = []

mikkelam

# -*- coding: utf-8 -*-
# pylint: disable=line-too-long, unused-argument, invalid-name, too-many-arguments, too-many-locals

"""
Utilities to support integration of Vowpal Wabbit and scikit-learn
"""
import numpy as np
import sklearn
from pyvw import vw
import re

mikkelam

import networkx as nx

def hamilton(G):
    F = [(G,[list(G.nodes())[0]])]
    n = G.number_of_nodes()
    while F:
        graph,path = F.pop()
        confs = []
        neighbors = (node for node in graph.neighbors(path[-1]) 
                     if node != path[-1]) #exclude self loops