np.random.multinomial

np.random.multinomial是numpy库中的一个函数，用于生成多项式分布的随机样本。它的参数包括n，pvals和size，其中n表示试验次数，pvals表示每个结果的概率，size表示生成的样本数量。函数返回一个数组，其中每个元素表示对应样本中每个结果出现的次数。

相关问题

#target一共9个类别。由于是字符型，定义一个函数将target的类别标签转为index表示，方便后面计算交叉熵 def target2idx(targets): target_idx = [] target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10'] for target in targets: target_idx.append(target_labels.index(target)) return target_idx #向量转化函数(提供参考，自行选择是否使用) def convert_to_vectors(c): m = len(c) k = np.max(c) + 1 y = np.zeros(m * k).reshape(m,k) for i in range(m): y[i][c[i]] = 1 return y #特征处理函数(提供参考，自行选择是否使用) def process_features(X): scaler = MinMaxScaler(feature_range=(0,1)) X = scaler.fit_transform(1.0*X) m, n = X.shape X = np.c_[np.ones((m, 1)), X] return X数据获取样例，可自行处理 X = np.array(data)[:,1:-1].astype(float) c = target2idx(data['target']) y = convert_to_vectors(c) #划分训练集和测试集比例在0.1-0.9之间 X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2)#模型训练及预测#计算指标，本指标使用加权的方式计算多分类问题，accuracy和recall相等，可将其原因写入报告 accuracy = accuracy_score(c_test, c_pred) precision = precision_score(c_test, c_pred,average = 'weighted') recall = recall_score(c_test, c_pred,average = 'weighted') f1 = f1_score(c_test, c_pred,average = 'weighted') print("accuracy = {}".format(accuracy)) print("precision = {}".format(precision)) print("recall = {}".format(recall)) print("f1 = {}".format(f1))补全代码

这段代码是一个完整的机器学习流程，包括了数据获取、数据预处理、模型训练和评估等步骤。以下是补全的代码：

# 导入必要的库和函数
import numpy as np 
import pandas as pd 
from sklearn.preprocessing import MinMaxScaler 
from sklearn.model_selection import train_test_split 
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score 

# 将目标变量中的类别标签转化为数字型的索引
def target2idx(targets): 
    target_idx = [] 
    target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10'] 
    for target in targets: 
        target_idx.append(target_labels.index(target)) 
    return target_idx 

# 将分类问题中的目标变量转化为独热编码向量
def convert_to_vectors(c): 
    m = len(c) 
    k = np.max(c) + 1 
    y = np.zeros(m * k).reshape(m,k) 
    for i in range(m): 
        y[i][c[i]] = 1 
    return y 

# 对特征进行预处理
def process_features(X): 
    scaler = MinMaxScaler(feature_range=(0,1)) 
    X = scaler.fit_transform(1.0*X) 
    m, n = X.shape 
    X = np.c_[np.ones((m, 1)), X] 
    return X 

# 数据获取及预处理
data = pd.read_csv('data.csv')
X = np.array(data)[:,1:-1].astype(float) 
c = target2idx(data['target']) 
y = convert_to_vectors(c) 
X = process_features(X) 

# 划分训练集和测试集
X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2)

# 模型训练
from sklearn.linear_model import LogisticRegression 
clf = LogisticRegression(random_state=0, multi_class='multinomial', solver='newton-cg')
clf.fit(X_train, c_train)

# 模型预测
c_pred = clf.predict(X_test)

# 计算指标
accuracy = accuracy_score(c_test, c_pred) 
precision = precision_score(c_test, c_pred,average = 'weighted') 
recall = recall_score(c_test, c_pred,average = 'weighted') 
f1 = f1_score(c_test, c_pred,average = 'weighted') 

# 输出结果
print("accuracy = {}".format(accuracy)) 
print("precision = {}".format(precision)) 
print("recall = {}".format(recall)) 
print("f1 = {}".format(f1))

该代码首先读入数据，然后对目标变量进行转化，特征进行预处理，再将数据划分为训练集和测试集。接着，使用LogisticRegression模型进行训练，并进行预测。最后，计算分类指标，分别输出accuracy、precision、recall和f1值。

from sklearn.linear_model import LogisticRegression from sklearn import metrics import numpy as np import pandas as pd data = pd.read_csv( 'final_data1.csv') Y = data.y X = data.drop('y', axis=1) xmin = X.min(axis=0) xmax = X.max(axis=0) X_norm = (X-xmin)/(xmax-xmin) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_norm, Y, test_size=0.2, random_state=42) clf = LogisticRegression(random_state=0,multi_class='multinomial') clf.fit(X_norm,Y) y_pred= clf.predict(X_test) y_pred= np.round(y_pred) 给出使用网格搜索（GridSearchCV）调上述代码的超参数的代码

要使用网格搜索（GridSearchCV）调整上述代码的超参数，可以按照以下步骤进行：

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
import numpy as np
import pandas as pd

# 读取数据
data = pd.read_csv('final_data1.csv')
Y = data.y
X = data.drop('y', axis=1)
xmin = X.min(axis=0)
xmax = X.max(axis=0)
X_norm = (X - xmin) / (xmax - xmin)

# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_norm, Y, test_size=0.2, random_state=42)

# 定义超参数的候选值
param_grid = {
    'C': [0.1, 1.0, 10.0],  # 正则化强度的候选值
    'penalty': ['l1', 'l2'],  # 正则化类型的候选值
    'solver': ['newton-cg', 'sag', 'saga', 'lbfgs']  # 求解器的候选值
}

# 创建Logistic回归模型
model = LogisticRegression(random_state=0, multi_class='multinomial')

# 使用网格搜索寻找最佳超参数组合
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)
grid_search.fit(X_train, y_train)

# 输出最佳超参数组合
print("Best parameters: ", grid_search.best_params_)

# 使用最佳超参数组合的模型进行预测
best_model = grid_search.best_estimator_
y_pred = best_model.predict(X_test)
y_pred = np.round(y_pred)

在上述代码中，我们首先读取数据并进行归一化处理，然后划分训练集和测试集。接下来，我们定义了超参数的候选值（C、penalty和solver），创建了Logistic回归模型。然后，使用GridSearchCV进行网格搜索，寻找最佳的超参数组合。通过交叉验证（cv=5），它会尝试所有可能的超参数组合，并返回最佳组合。最后，我们使用最佳超参数组合的模型进行预测。

你可以根据自己的需求修改超参数的候选值，并根据实际情况选择合适的参数范围。