从HTML表单获取逗号分隔值：转换为NumPy数组并用于机器学习预测

import numpy as np

input_string = "17.99,10.38,122.8"
v1 = np.array((input_string))
print(v1)
# 输出: ['17.99,10.38,122.8']
print(v1.dtype)
# 输出: <U... (表示Unicode字符串类型)
print(v1.shape)
# 输出: () 或 (1,) (取决于NumPy版本和上下文，但关键是它不是一个包含浮点数的数组)

ValueError: Expected 2D array, got 1D array instead:
array=['17.99,10.38,122.8,1001,...'].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

import numpy as np

# 假设 request.GET['n1'] 获取到的字符串是：
input_data_string = "17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189"

# 1. 分割字符串
string_list = input_data_string.split(",")
print("分割后的字符串列表:", string_list[:5], "...")

# 2. 转换为浮点数列表，并直接创建NumPy一维数组
v1_processed = np.array([float(num_str) for num_str in string_list])
print("转换为浮点数数组:", v1_processed[:5], "...")
print("数组形状:", v1_processed.shape)

# 假设 v1_processed 已经是一个一维的NumPy数组，例如形状为 (30,)
# v1_processed = np.array([float(num_str) for num_str in input_data_string.split(",")])

v1_final = v1_processed.reshape(1, -1)
print("重塑后的数组:", v1_final)
print("重塑后的数组形状:", v1_final.shape)

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from django.shortcuts import render # 假设为Django环境

def output(request):
    # 加载数据和训练模型（这部分代码保持不变）
    # 实际应用中，模型通常在应用启动时加载或作为单例管理，而非每次请求都重新训练
    dff = pd.read_csv(r'C:\Users\Downloads\data.csv')
    y = dff['diagnosis'].values
    x = dff.drop('diagnosis', axis=1).values
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.40, random_state=42) # 添加random_state保证可复现性
    model = LogisticRegression(max_iter=1000) # 增加max_iter以避免收敛警告
    model.fit(x_train, y_train)

    # 获取用户输入字符串
    input_string = request.GET.get('n1', '') # 使用.get()方法获取，并提供默认值以避免KeyError

    # --- 关键修改部分开始 ---
    pred1 = ""
    if not input_string:
        pred1 = "输入数据不能为空。请提供逗号分隔的数字。"
    else:
        try:
            # 1. 分割字符串并转换为浮点数数组
            v1_processed = np.array([float(num) for num in input_string.split(",")])

            # 2. 重塑数组为 (1, -1) 形状
            v1_final = v1_processed.reshape(1, -1)

            # 检查特征数量是否匹配模型期望
            if v1_final.shape[1] != x_train.shape[1]:
                pred1 = f"输入特征数量不匹配