Fri Apr 14 09:35:00 UTC 2023 inscode

main.py

-print('欢迎来到 InsCode')
-import os
-print(os.path.dirname('./'))
-import numpy
-print(numpy.__version__)
-import 
\ No newline at end of file
+import numpy as np
+
+# 生成一个10x10的随机矩阵
+rand_mat = np.random.rand(10, 10)
+
+# 输出矩阵的形状和数据类型
+print(rand_mat)
+print("Matrix shape:", rand_mat.shape)
+print("Matrix data type:", rand_mat.dtype)
+
+# 计算矩阵的平均值、最小值、最大值和总和
+mean = np.mean(rand_mat)
+min_val = np.min(rand_mat)
+max_val = np.max(rand_mat)
+sum_val = np.sum(rand_mat)
+
+# 输出计算结果
+print("Mean value:", mean)
+print("Minimum value:", min_val)
+print("Maximum value:", max_val)
+print("Sum value:", sum_val)

test.json

 {
     "$schema": "https://www.quickapi.cloud/schema.json",
     "pipeline": [
-        {
-            "name": "redis",
-            "action": "get",
-            "key": "user_${param.session_id}",
-            "output": "user",
-            "json": true,
-            "property": "common_redis"
-        },
         {
             "name": "pipeline",
             "uri": "/test",
@@ -33,5 +25,32 @@
                 }
             ]
         }
+    ],
+    "response_code": [
+        {
+            "name": "success",
+            "code": 200,
+            "message": "success"
+        },
+        {
+            "name": "fail",
+            "code": 500,
+            "message": "fail"
+        }
+    ],
+    "property": {
+        "common_redis": {
+            "url": "redis://127.0.0.1:6379"
+        },
+        "db_interview": {
+            "driver": "com.mysql.jdbc.Driver",
+            "url": "jdbc:mysql://localhost:3306/xxx?useUnicode=true&characterEncoding=utf8",
+            "user_name": "root",
+            "password": "123456"
+        }
+    },
+    "include": [
+        "config/db.json",
+        "http://localhost:8081/api/b/v1/t_config?app=test"
     ]
 }
\ No newline at end of file

test2.py

+from sklearn.datasets import fetch_20newsgroups
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score
+
+# 加载新闻数据集
+news_data = fetch_20newsgroups(subset='all', categories=['alt.atheism', 'talk.religion.misc'])
+
+print(news_data.data[0])
+
+# 将数据集分为训练集和测试集
+train_data = fetch_20newsgroups(subset='train', categories=['alt.atheism', 'talk.religion.misc'])
+test_data =  fetch_20newsgroups(subset='test',  categories=['alt.atheism', 'talk.religion.misc'])
+
+print('---------------')
+print(test_data.data[0])
+print('---------a-------a-------')
+print(test_data.target[0])
+
+# 将文本转换为特征向量
+vectorizer = CountVectorizer()
+train_features = vectorizer.fit_transform(train_data.data)
+test_features = vectorizer.transform(test_data.data)
+
+# 训练朴素贝叶斯分类器
+clf = MultinomialNB()
+clf.fit(train_features, train_data.target)
+
+# 预测测试集并计算准确率
+pred = clf.predict(test_features)
+accuracy = accuracy_score(test_data.target, pred)
+print("Accuracy:", accuracy)