0.0.1

KNN.py

@@ -0,0 +1,33 @@
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+import os
+
+
+class KNNClassifier:
+    def __init__(self,
+                 backbone='KNNClassifier',
+                 n_neighbors=10,
+                 ):
+        self.backbone = backbone
+        # 获取外部运行py的绝对路径
+        self.cwd = os.path.dirname(os.getcwd())
+        # 获取当前文件的绝对路径
+        self.file_dirname = os.path.dirname(os.path.abspath(__file__))
+        self.dataset = ''
+        self.x_train, self.x_test, self.y_train, self.y_test = 0, 0, 0, 0
+        self.model = KNeighborsClassifier(n_neighbors=n_neighbors)
+
+    def train(self):
+        self.model.fit(self.x_train, self.y_train)
+        acc = self.model.score(self.x_test, self.y_test)
+        print('准确率为：{}%'.format(acc * 100))
+
+    def inference(self, data):
+        result = self.model.predict(data)
+        print(result)
+        print("分类结果：{}".format(self.dataset['target_names'][result]))
+
+    def load_dataset(self, dataset):
+        self.dataset = dataset
+        self.x_train, self.x_test, self.y_train, self.y_test = \
+            train_test_split(self.dataset['data'], self.dataset['target'], test_size=0.2, random_state=0)

Mlearing_Edu.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import os
+
+class MMMlearing:
+    def __init__(self,
+                 backbone='RandomForest'
+                 ):
+        self.backbone = backbone
+        # 获取外部运行py的绝对路径
+        self.cwd = os.path.dirname(os.getcwd())
+        # 获取当前文件的绝对路径
+        self.file_dirname = os.path.dirname(os.path.abspath(__file__))
+        self.model = RandomForestClassifier()
+
+    def train(self, seed=0):
+        np.random.seed(seed)
+        dataset = pd.read_csv(self.dataset_path, sep=',', header=None).values
+        np.random.shuffle(dataset)
+        data, label = dataset[:,:-1],dataset[:,-1]
+        train_index = int((1-self.test_size) * len(dataset))
+        train_data, train_label = data[:train_index, :], label[:train_index]
+        self.test_set = {
+            'data':data[train_index:, :],
+            'label':label[train_index:]
+        }
+        self.model.fit(train_data, train_label)
+
+    def inference(self):
+        pred = self.model.predict(self.test_set['data'])
+        acc = accuracy_score(self.test_set['label'], pred)
+        print('准确率为：{}%'.format(acc*100))
+
+    def load_dataset(self, path, test_size=0.2):
+        self.dataset_path = path
+        self.test_size = test_size

PCA.py

@@ -0,0 +1,30 @@
+from sklearn.decomposition import PCA
+import os
+
+
+class PCA:
+    def __init__(self,
+                 backbone='KNNClassifier',
+                 n_components='mle',
+                 ):
+        self.backbone = backbone
+        # 获取外部运行py的绝对路径
+        self.cwd = os.path.dirname(os.getcwd())
+        # 获取当前文件的绝对路径
+        self.file_dirname = os.path.dirname(os.path.abspath(__file__))
+        self.dataset = ''
+        self.x_train, self.x_test = 0, 0
+        self.model = PCA(n_components=n_components)
+
+    def train(self):
+        self.model.fit(self.dataset)
+        print(self.model.explained_variance_ratio_)
+        # 返回所保留的n个成分各自的方差百分比,这里可以理解为单个变量方差贡献率。
+
+    def inference(self, data):
+        self.model.fit_transform(data)
+        print(self.model.n_features_)
+        print(self.model.n_samples_)
+
+    def load_dataset(self, dataset):
+        self.dataset = dataset

ml_demo.py

@@ -0,0 +1,25 @@
+from sklearn.datasets import load_wine
+from KNN import KNNClassifier
+from PCA import PCA
+import numpy as np
+
+
+def knn_demo():
+    model = KNNClassifier(n_neighbors=10)
+    model.load_dataset(dataset=load_wine())
+    model.train()
+    test_data = np.array([[11.8, 4.39, 2.39, 29, 82, 2.86, 3.53, 0.21, 2.85, 2.8, 0.75, 3.78, 490]])
+    model.inference(test_data)
+
+
+def pca_demo():
+    data = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
+    model = PCA(n_components='mle')
+    model.load_dataset(dataset=data)
+    model.train()
+    model.inference(data)
+
+
+if __name__ == '__main__':
+    knn_demo()
+    pca_demo()