zhpmatrix

def dropout(x, level, noise_shape=None, seed=None):
    """Sets entries in `x` to zero at random,
    while scaling the entire tensor.

    # Arguments
        x: tensor
        level: fraction of the entries in the tensor
            that will be set to 0.
        noise_shape: shape for randomly generated keep/drop flags,
            must be broadcastable to the shape of `x`

zhpmatrix

# initialize the parameters of logistic regression

def initialize_with_zeros(dim):
    """
    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.
    
    Argument:
    dim -- size of the w vector we want (or number of parameters in this case)
    
    Returns:

zhpmatrix

返回Logistic Regression中的预测错误的样本：

float(sum( labels != (preds > 0.0) ) ) / len(labels)

核心逻辑：对于Sigmoid函数，y = 1 / 1 + exp(-x)，当x > 0 时，y = 1, 当 x < 0 时, y = 0.

zhpmatrix

def custom_loss(y_pre,D_label): #别人的自定义损失函数
    label=D_label.get_label()
    penalty=2.0
    grad=-label/y_pre+penalty*(1-label)/(1-y_pre) #梯度
    hess=label/(y_pre**2)+penalty*(1-label)/(1-y_pre)**2 #2阶导
    return grad,hess

zhpmatrix

from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score

import pandas as pd
import scipy as sp
import xgboost as xgb

import matplotlib.pyplot as plt

zhpmatrix

def f1_error(preds,dtrain):
    label=dtrain.get_label()
    preds = 1.0/(1.0+np.exp(-preds))
    pred = [int(i >= 0.5) for i in preds]
    tp = sum([int(i == 1 and j == 1) for i,j in zip(pred,label)])
    precision=float(tp)/sum(pred)
    recall=float(tp)/sum(label)
    return 'f1-score',2 * ( precision*recall/(precision+recall) )

zhpmatrix

#!/usr/bin/python
import numpy as np
import xgboost as xgb
###
# advanced: customized loss function
#
print ('start running example to used customized objective function')

dtrain = xgb.DMatrix('agaricus.txt.train')
dtest = xgb.DMatrix('agaricus.txt.test')

zhpmatrix

#include"mpi.h"
int main(int argc,char *argv[])
{
       char message[20]="";
       int myrank;
       MPI_Status status;
       MPI_Init(&argc,&argv);
       MPI_Comm_rank(MPI_COMM_WORLD,&myrank);
       if(myrank==0)
       {/*先将字符串拷贝到发送缓冲区message中，然后调用MPI_Send语句将它发出，用

zhpmatrix

#include <iostream>
#include <boost/tuple/tuple.hpp>
#include <boost/tuple/tuple_comparison.hpp>
#include <boost/tuple/tuple_io.hpp>
using namespace boost;
int main(){
	tuple<int, char, float> t(2, 'a', 0.9);
　 	std::cout << t << std::endl;
　	return 0;
}

zhpmatrix

#################################################################
#desc: find the number of ways to divide series into diff sets
#author: zhpmatrix
#date: 2017-05-11
#################################################################

from itertools import combinations
def c(n,k):
	return len([val for val in combinations(range(n),k)])