代码解释：def sendfs(email=None, title='消息标题', content='消息内容'): """ :param email: 如果为None,则从当前设备关联的ms账号取，否则使用该参数 :param title: :param content: :return: """ if not email: email = getMyEmail() if not email: reprint("获取设备所有人邮箱失败，无法发送飞书消息，原因:1.当前设备未入库 2.未设置设备使用人3.或者测试平台关联的邮箱不存在") return try: senddata = {'email': email, 'title': title, 'content': content} req = requests.request(method='POST', url=f'{getapiurl()}/v1/config/sendfs_new', data=senddata) result = req.json() fsresult = result['data'] if fsresult['code'] == 0: reprint(f'email:{email}, 飞书消息发送成功') elif fsresult['code'] == 230001: reprint(f'email:{email}, 邮箱错误或者指定账户已停用') else: reprint(f'email:{email}, 飞书消息发送失败') except Exception as e: reprint(str(e)+",飞书消息发送失败")

该代码段为Python代码，包括一个名为“sendfs”的函数，其作用是发送飞书消息。函数包含三个参数：email（邮件），title（消息标题）和content（消息内容）。如果没有提供电子邮件，则使用当前设备关联的MS账户。发送数据是从请求中提交的，并且使用POST方法发送到指定的API URL。如果消息发送成功，则显示成功消息；如果失败，则显示错误消息。

相关问题

def svmModel(x,y,param): svmmodel=svm.SVC(C=param[1],kernel=param[0]) if param[0] == 'rbf': svmmodel.gamma = param[2] title = '高斯核，C=%.1f，$\gamma$ =%.1f' % (param[1], param[2]) else: title = '线性核，C=%.1f' % param[1] svmmodel.fit(x,y) y_predict=svmmodel.predict(x) accuracyscore=accuracy_score(y,y_predict) return svmmodel,title,accuracyscore if __name__=='__main__': path='D:/data/iris.data' data=pd.read_csv(path,header=None) x=data[list(range(2,4))] y=data[4].replace(['Iris-versicolor','Iris-virginica'],[0,1]) clf_param=(('linear',(0.1)),('rbf',(1,0.1)),('rbf',(5,5)),('rbf',(10,10))) for i,param in enumerate(clf_param): clf,title,accuracy_score=svmModel(x,y,param) y_hat=clf.predict(x) print(title) print('准确率：',accuracy_score) print('支持向量的数目：',clf.n_support_)报错TypeError: '<' not supported between instances of 'int' and 'str'

这个错误可能是由于使用了不同类型的数据进行了比较。具体来说，在输出 `svmModel` 函数中的 title 时，使用了字符串格式化，但是其中可能包含了不同类型的参数（整数和字符串）。这会导致类型错误。可以在字符串格式化时，将参数转换为字符串类型。修改后的代码如下：

def svmModel(x, y, param):
    svmmodel = svm.SVC(C=param[1], kernel=param[0])
    if param[0] == 'rbf':
        svmmodel.gamma = param[2]
        title = '高斯核，C=%.1f，$\gamma$ =%.1f' % (param[1], param[2])
    else:
        title = '线性核，C=%.1f' % param[1]
    svmmodel.fit(x, y)
    y_predict = svmmodel.predict(x)
    accuracyscore = accuracy_score(y, y_predict)
    return svmmodel, title, accuracyscore


if __name__ == '__main__':
    path = 'D:/data/iris.data'
    data = pd.read_csv(path, header=None)
    x = data[list(range(2, 4))]
    y = data[4].replace(['Iris-versicolor', 'Iris-virginica'], [0, 1])
    clf_param = (('linear', (0.1)), ('rbf', (1, 0.1)), ('rbf', (5, 5)), ('rbf', (10, 10)))
    for i, param in enumerate(clf_param):
        clf, title, accuracy_score = svmModel(x, y, param)
        y_hat = clf.predict(x)
        print(str(title))  # 将 title 转换为字符串类型
        print('准确率：', accuracy_score)
        print('支持向量的数目：', clf.n_support_)

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] x = data.drop(['y'], axis=1) print('************************输出新的特征集数据***************************') print(x.head()) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) def relu(x): output=np.maximum(0, x) return output def relu_back_propagation(derror_wrt_output,x): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,x): if activation_choose == 'relu': return relu(x) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def __init__(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b() def set_learning_rate(self,learning_rate): self.learning_rate=learning_rate def set_num_iterations(self, num_iterations): self.num_iterations = num_iterations def set_xy(self, input, expected_output): self.x = input self.y = expected_output

这段代码存在两个问题：

1. 在import语句中没有使用逗号分隔符，应该在每个import语句之间添加逗号分隔符。
2. 在函数set_xy()的定义中，函数体没有缩进，应该在函数体内部添加缩进。

以下是修改后的代码：

import pandas as pd, warnings
import sklearn.datasets, sklearn.linear_model
import matplotlib, matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx')
print(data.info())

fig = plt.figure(figsize=(10, 8))
sns.heatmap(data.corr(), cmap="YlGnBu", annot=True)
plt.title('相关性分析热力图')
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.show()

y = data['y']
x = data.drop(['y'], axis=1)

print('************************输出新的特征集数据***************************')
print(x.head())

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

def relu(x):
    output=np.maximum(0, x)
    return output

def relu_back_propagation(derror_wrt_output,x):
    derror_wrt_dinputs = np.array(derror_wrt_output, copy=True)
    derror_wrt_dinputs[x <= 0] = 0
    return derror_wrt_dinputs

def activated(activation_choose,x):
    if activation_choose == 'relu':
        return relu(x)

def activated_back_propagation(activation_choose, derror_wrt_output, output):
    if activation_choose == 'relu':
        return relu_back_propagation(derror_wrt_output, output)

class NeuralNetwork:
    def __init__(self, layers_strcuture, print_cost = False):
        self.layers_strcuture = layers_strcuture
        self.layers_num = len(layers_strcuture)
        self.param_layers_num = self.layers_num - 1
        self.learning_rate = 0.0618
        self.num_iterations = 2000
        self.x = None
        self.y = None
        self.w = dict()
        self.b = dict()
        self.costs = []
        self.print_cost = print_cost
        self.init_w_and_b()
    
    def set_learning_rate(self,learning_rate):
        self.learning_rate=learning_rate 
    
    def set_num_iterations(self, num_iterations):
        self.num_iterations = num_iterations 
    
    def set_xy(self, input, expected_output):
        self.x = input
        self.y = expected_output