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  • Binarize
    2021-03-14 15:30:46
    import math
    import cmath
    import sys
    import string
    import bisect
    import heapq
    import copy
    from queue import Queue,PriorityQueue,LifoQueue
    from itertools import permutations,combinations
    from collections import Counter,deque
    from functools import cmp_to_key
    更多相关内容
  • label_binarize

    千次阅读 2021-11-20 20:56:34
    label_binarize(y, *, classes, neg_label=0, pos_label=1, sparse_output=False): """Binarize labels in a one-vs-all fashion Several regression and binary classification algorithms are available in ...
     label_binarize(y, *, classes, neg_label=0, pos_label=1,
                       sparse_output=False):
        """Binarize labels in a one-vs-all fashion
        Several regression and binary classification algorithms are
        available in scikit-learn. A simple way to extend these algorithms
        to the multi-class classification case is to use the so-called
        one-vs-all scheme.
        以一对多的方式二值化标签
         scikit-learn 中提供了几种回归和二元分类算法。 将这些算法扩展到多类分类情况的一种简单方法是使用所谓的“一对一”方案。
        This function makes it possible to compute this transformation for a
        fixed set of class labels known ahead of time.
        此函数可以为提前已知的一组固定类标签计算此转换。
        Parameters参数
        ----------
        y : array-like
            Sequence of integer labels or multilabel data to encode.#要编码的整数标签或多标签数据的序列。
        classes : array-like of shape [n_classes]
            Uniquely holds the label for each class.#唯一地保存每个类的标签。
        neg_label : int (default: 0)
            Value with which negative labels must be encoded.#负标签必须编码的值。
        pos_label : int (default: 1)
            Value with which positive labels must be encoded.#必须对正标签进行编码的值。
        sparse_output : boolean (default: False),
            Set to true if output binary array is desired in CSR sparse format#如果需要 CSR 稀疏格式的输出二进制数组,则设置为 true
        Returns
        -------
        Y : numpy array or CSR matrix of shape [n_samples, n_classes]
            Shape will be [n_samples, 1] for binary problems.#对于二元问题,形状将为 [n_samples, 1]。
        Examples
        --------
        >>> from sklearn.preprocessing import label_binarize
        >>> label_binarize([1, 6], classes=[1, 2, 4, 6])
        array([[1, 0, 0, 0],
               [0, 0, 0, 1]])
        The class ordering is preserved:
        >>> label_binarize([1, 6], classes=[1, 6, 4, 2])
        array([[1, 0, 0, 0],
               [0, 1, 0, 0]])
        Binary targets transform to a column vector
        >>> label_binarize(['yes', 'no', 'no', 'yes'], classes=['no', 'yes'])
        array([[1],
               [0],
               [0],
               [1]])
        See also
        --------
        LabelBinarizer : class used to wrap the functionality of label_binarize and
            allow for fitting to classes independently of the transform operation
        """

    记录一下遇到的问题,原来是三分类的问题使用这个函数没有问题,但现在要修改适配二分类的问题,就遇到很多bug,代码里要修改的地方比较多,能修改好的话就更新。

    解决了,总结下。

    label_binarize对于两个以上的分类,可以将1维转化为多维,对于二分类,就还是一维,为了适应下面的代码,需要将二分类的转化为二维才行。

    np_utils.to_categorical(train, 2)将原来标签是一列的[1,0,0,0,1...]的转换为一行两列的独热码。
    
    人傻了,想了老半天,代码看了好多遍,搜了好多东西,都理解透了,差点想重新写了,然后一行代码搞定,道行还是太浅!。
    
    

    展开全文
  • Binarize :在给定阈值的情况下对一列连续特征进行二值化 class pyspark.ml.feature.Binarizer(threshold=0.0, inputCol=None, outputCol=None)[[source]]...

    Binarize :在给定阈值的情况下对一列连续特征进行二值化

    class pyspark.ml.feature.Binarizer(threshold=0.0, inputCol=None, outputCol=None)[[source]](https://spark.apache.org/docs/2.4.5/api/python/pyspark.ml.html#pyspark.ml.feature.Binarizer

    threshold:用于单列,thresholds:用于多列(当前版本2.4.5不支持)

    ​ threshold即为阈值

    inputCol:用于单列,inputCols:用于多列(当前版本2.4.5不支持)

    01.创建对象

    from pyspark.sql import SparkSession
    from pyspark.ml.feature import Binarizer
    spark = SparkSession.builder.config("spark.Driver.host","192.168.1.4")\
        .config("spark.ui.showConsoleProgress","false")\
        .appName("Binarize").master("local[*]").getOrCreate()
    

    02.创建数据

    data = spark.createDataFrame([
        (0.1,),
        (2.3,),
        (1.1,),
        (4.2,),
        (2.5,),
        (6.8,),
    ],["values"])
    data.show()
    

    ​ 输出结果:

    +------+
    |values|
    +------+
    |   0.1|
    |   2.3|
    |   1.1|
    |   4.2|
    |   2.5|
    |   6.8|
    +------+
    

    03.创建一个Binarize对象,参数中指定输入列,阈值和输出列

    binarizer = Binarizer(threshold=2.4,inputCol="values",outputCol="features")
    

    04.转换原始数据并查看结果

    res = binarizer.transform(data)
    res.show()
    

    ​ 输出结果

    +------+--------+
    |values|features|
    +------+--------+
    |   0.1|     0.0|
    |   2.3|     0.0|
    |   1.1|     0.0|
    |   4.2|     1.0|
    |   2.5|     1.0|
    |   6.8|     1.0|
    +------+--------+
    

    05.查看结构

    res.printSchema()
    

    1输出结果:

    root
     |-- values: double (nullable = true)
     |-- features: double (nullable = true)
    
    展开全文
  • The Problem: Given an integer, your program should binarize it. 输入描述: The first input line contains a positive integer,n, indicating the numberof values to binarize. The values are on the ...

    时间限制:C/C++ 1秒,其他语言2秒

    空间限制:C/C++ 262144K,其他语言524288K
    64bit IO Format: %lld

    题目描述 

    Professor Boolando can only think in binary, or more specifically, in powers of 2. He converts any number you give him to the smallest power of 2 that is equal to or greater than your number. For example, if you give him 5, he converts it to 8; if you give him 100, he converts it to 128; if you give him 512, he converts it to 512.

     

    The Problem:

     

    Given an integer, your program should binarize it.

    输入描述:

     

    The first input line contains a positive integer,n, indicating the numberof values to binarize. The values are on the followingninput lines, one per line. Each input will contain an integer between2 and 100,000 (inclusive).

    输出描述:

     

    At thebeginning of each testcase, output “Inputvalue:v”wherevis the input value. Then,on the next output line, print the binarized version. Leave a blank line after the output for each test case.

    示例1

    输入

    复制

    3
    900
    16
    4000

    输出

    复制

    Input value: 900
    1024
    
    Input value: 16
    16
    
    Input value: 4000
    4096

     判断某个数是否为2的次方

    bool is2N(int n)
    {
        if(n&(n-1))
            return false;
        else
            return true;
    }
    求某个数至少为2的多少次方

               int cn=-1;
               while(v)
               {
                    v=v>>1;
                    cn++;
                }
                cout<<cn<<endl;

    #include<cstdio>
    #include<cstring>
    #include<algorithm>
    #include<iostream>
    #include<set>
    #include<queue>
    #include<stack>
    #include<map>
    #include<cmath>
    using namespace std;
    typedef long long ll;
    #define Inf 0xfffffff
    #define N 501000
    #define eps 1e-7
    using namespace std;
    
    bool is2N(int n)
    {
        if(n&(n-1))
            return false;
        else
            return true;
    }
    
    int main()
    {
        int n;
        cin>>n;
        while(n--)
        {
            //cout<<n<<endl;
            int v;
            cin>>v;
            cout<<"Input value: "<<v<<endl;
            if(is2N(v))
            {
                cout<<v<<endl;
            }
            else
            {
                int cn=-1;
                while(v)
                {
                    v=v>>1;
                    cn++;
                }
                cout<<pow(2,cn+1)<<endl;
            }
            if(n!=0)
            {
                cout<<endl;
            }
        }
        return 0;
    }
    
    

     

    展开全文
  • Binarize It 题目链接:https://ac.nowcoder.com/acm/contest/12794/A 题目描述 Professor Boolando can only think in binary, or more specifically, in powers of 2. He converts any number you give him to the...
  • sklearn文档: ... sklearn.preprocessing.label_binarize 类的顺序被保留: from sklearn.preprocessing import label_binarize label_binarize([1, .
  • binarize方法 lab = label_binarize(df7['key'],classes = ['a','b','c']) lab array([[0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 1, 0]]) columns = df7.join(pd.DataFrame(lab)).rename(columns...
  • 就像StandardScaler和Normalizer类一样,preprocessing模块也为我们提供了一个方便的额binarize进行数值特征的二值化。 Sparse input normalize函数和Normalizer类都接受dense array-like and sparse matrics ...
  • 如下图所示,报错为TypeError: JayChou() missing 1 required keyword-only argument: ‘c’ 翻译过来是:TypeError:JayChou()缺少1个仅限关键字的参数:“c” 报错代码: #coding=utf-8 def JayChou(a, *b, c): ...
  • moses binarize-all问题

    2015-06-04 17:18:49
    要想在EMS(Experiment Management System)中运用Word Lattice进行调参或解码,就不得不将配置文件中binarize-all开关打开,并且指定ttable-binarizer, 例如ttable-binarizer = "$moses-bin-dir/CreateOnDiskPt 1 1...
  • y_binarize = label_binarize(y_train, classes = class_names) # 标签热编码 model= LogisticRegression(multi_class = params['multi_class'],solver=params['solver'],penalty=params['penalty'],C=float(params...
  • sklearn.preprocessing.Binarizer

    千次阅读 2017-07-11 16:12:59
    Binarizer类和binarize方法根据指定的阈值将特征二值化,小于等于阈值的,将特征值赋予0,大于特征值的赋予1,其阈值threshold默认都为0 ①binarize方法:sklearn.preprocessing.binarize(X, threshold=0.0, copy=...
  • y_train) model.score(X_test, y_test) 使用手动网格化找最优超参数 best_score = 0 for binarize in np.arange(0, 1.1, 0.1): for alpha in np.arange(0, 1.1, 0.1): model = BernoulliNB(binarize=binarize, ...
  • 和roc曲线的绘制,报错提示只有二分类才可以,因此我们将多分类问题转化为几个二分类问题,分类别进行画图 这里参考了许多网上搜来的帖子的代码,还有sklearn官网上的代码 y = label_binarize(y, classes=[0, 1, 2]...
  • 为1×n_classes array-like (2)函数: 根据阈值对数据进行"二值化"(binarize):[<X_tr>=]sklearn.preprocessing.binarize([,threshold=0.0,copy=True]) #参数说明:其他参数同class sklearn.preprocessing....
  • from sklearn.preprocessing import label_binarize from sklearn import metrics mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False names = ['Age', 'Number of ...
  • python绘制ROC曲线,计算AUC

    千次阅读 2021-10-26 10:49:35
    所以需要对标签值作如下二值化处理 n_class = len(data['accept'].unique()) #accept是标签列 y_test_one_hot = label_binarize(y_test, classes=np.arange(n_class)) # 将标签值映射成one-hot编码 #print(y_test_...
  • for_Feature_Scaling.csv') data_set.head() # here Features - Age and Salary columns # are taken using slicing # to binarize values age = data_set.iloc[:, 1].values salary = data_set.iloc[:, 2].values ...
  • 数据处理时有时需要将离散特征进行独热编码或者哑变量编码。两者的区别如下所示 ...y_test_hot = label_binarize(Y_test,classes=(1,2,3)) print(y_test_hot[0:5]) print(y_test_hot.ravel()[0:15])
  • valid) Y_pred = [np.argmax(y) for y in Y_pred] # 取出y中元素最大值所对应的索引 Y_valid = [np.argmax(y) for y in Y_valid] # Binarize the output Y_valid = label_binarize(Y_valid, classes=[i for i in ...
  • test_BernoulliNB_binarize(X_train,X_test,y_train,y_test) # 调用 test_BernoulliNB_binarize 递增式学习partial_fit方法: 朴素贝叶斯可以用来解决大规模的分类问题,上述三个分类器都有.partial_fit方法,...
  • 数据预览及数据处理

    千次阅读 2019-06-05 13:58:37
    #marital 转化为四列: marital_divorced, marital_married,marital_single,marital_unknown 方法2:label_binarize # 方法2 from sklearn.preprocessing import label_binarize classes=['divorced', 'married','...

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