fjqlldg的专栏

import numpy as np from sklearn.datasets import load_iris from sklearn.linear_model import Perceptron iris = load_iris() X = iris.data[:,(2,3)] y = (iris.target == 0).astype(np.int) per_clf = Perceptron() per_clf.fit(X,y) y_pred = per_clf.predict([[2,0.

一、图片读取和存储 import cv2 import numpy as np image = cv2.imread("BigBuck.png") print(image) 二、图像的基本变换 1、灰度化 import cv2 import numpy as np img = cv2.imread("BigBuck.png") print(img.shape) #使用cv2.cvtColor()方法将彩色图片转换为灰度图片 gray_img = cv2.cvtColor(img, cv2.

运行结果： 完整代码： #导入K近邻算法所使用的包 from sklearn import datasets from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score fro...

一、数据集分成训练集与评估集 1、按2:1把数据分成训练集与评估集合 import pandas as pd import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression iris = datasets.load_iris() names = ['sepa

一、数据特征选择目的 1、降低数据的拟合度 2、提高算法精度 3、减少训练时间 二、数据特征方法 1、单变量特征（卡方检验） import pandas as pd import numpy as np from sklearn import datasets from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 iris = datasets.load_ir

一、最值化调整数据 import pandas as pd import numpy as np from sklearn import datasets import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler iris = datasets.load_iris() names = ['separ-length','separ-width','petal-length','petal-width'

机器学习项目过程 导入数据 数据特征 数据可视化 评估算法 实施预测 分析结果 一、导入类库及数据 1、导入类库 import pandas as pd from sklearn import datasets import matplotlib.pyplot as plt 2、导入数据 names = ['separ-length','separ-width','petal-length','petal-width','class'] data = pd.read_csv(r'D