原创 Python:解決字典在迭代中無法添加的問題,實現遍歷動態字典的目的。
import numpy as np from collections import OrderedDict Dict = OrderedDict() a =[2,86,94,2,75,1,23,89] for i in range
原创 Python:shuffle(a) 打亂array或list的順序,原始的可迭代對象中元素順序打亂,但原始對象的類別不變
import numpy as np a = np.array(range(20)) print("a=",a) b = np.arange(20) print("b=",b) s_a = np.random.shuffle(a) s
原创 Python:numpy random choice
import numpy as np a = np.array([100,200,300,500,400,701,852,965]) idx = np.random.choice(a,2,replace=False) print(i
原创 Python:獲取array滿足給定條件的元素的索引
使用np.argwhere import numpy as np a = np.array([1,2,3,4,5,6,1,2,3]) b = np.argwhere(a == 1) print(b) 輸出: [[0] [6]] 使
原创 Python:手工生成兩個環的二維數據
import numpy as np import pandas as pd import math import matplotlib.pyplot as plt num_data = 1000 center = np.array(
原创 Python:對一維array中的數值進行從大到小排序
import numpy as np a = np.array([5,6,8,2,1,7,5,3,90,78,62,5,4,2,9,4]) # b = a.sort(axis=0,kind='quicksort',order=None)
原创 Python:sklearn內置數據集還可以這麼調用,一步完成!
X, y = load_iris(return_X_y=True)
原创 Python:十折交叉驗證sklearn KFold 的使用
from sklearn.model_selection import KFold from sklearn.datasets import load_iris from sklearn.ensemble import RandomFo
原创 Python:讀取COIL20.mat文件
import scipy.io as scio import numpy as np path = r'E:\dataset\clusterData\label.mat' path1 = r'E:\dataset\clusterDa
原创 Python:對array 使用 list()不會改變array的類別
import numpy as np a = np.array([1,2,3,4,5]) b = list(a) print(type(a)) print(type(b)) print(a) print(b)
原创 Java:將csv文件轉化爲arff文件
package classifier; import weka.core.Instances; import weka.core.converters.ConverterUtils.DataSource; import weka.cor
原创 Python:下載輪子whl的地方
Unofficial Windows Binaries for Python Extension Packages https://www.lfd.uci.edu/~gohlke/pythonlibs/#tensorflow
原创 Python:將MNIST存儲爲CSV文件
import numpy as np import pandas as pd from sklearn import datasets # X,y = datasets.load_breast_cancer(return_X_y=Tr
原创 Java:求開根號,向下取整,將double轉化爲int型
package classifier; public class getSqrt { public static void main(String[] args){ double a = 15;
原创 Python:使用numpy.unique() 統計list或array中所包含的元素,即返回不重複元素
import numpy as np # a = [1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,6] a = np.array([1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,6])