position

def conflict(state, nextX): print('state:',state) nextY = len(state) for i in range(nextY): print("i=",i) if abs(state[i]-nextX) in (0, nextY-i): print('confict') return True return False def queens(num=8, state=()): for position in range(num): print('***') print('pos:',position) if not conflict(state, position): if len(state) == num-1: print('back to 1st_space_____________position:',position) yield (position,) else: print('Into 2nd_space__________recursive') for result in queens(num, state + (position,)): print('membersihip position:',position) print('membership result:',result) yield

原创YouTube@ Online Tutorials css代码： * { margin: 0; padding: 0; box-sizing: border-box; font-family: 'Poppins', sans-serif; } body { display: flex; justify-content: center; align-items: center; min-height: 100vh; background: #03080a; } .container { width: 80%; padding: 20px; } .container h2 { width: 100%; color: #45f3ff; font-size: 36px; text-align: center; margin-bottom: 10px; } .container .row100 { position: relative; width: 100%; display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); } .container .row100 .col { position: relative; width: 100%; padding: 0 10px; margin:

原文策略源码如下： #HS300–随机森林拐点识别 import math import numpy as np import pandas as pd from pandas import DataFrame,Series import pickle import time import datetime import jqdata import talib as tb import sklearn from sklearn.decomposition import PCA from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import AdaBoostClassifier,GradientBoostingClassifier,RandomForestRegressor,RandomForestClassifier from sklearn import cross_validation, metrics,svm,preprocessing from sklearn.metrics import mean_squared_error from sklearn.cross_validation import train_test_split from sklearn.grid_search