def knn_predict(train_set, test_instance, k): distances = [] for train_instance in train_set: dist = euclidean_distance(test_instance, train_instance) distances.append((train_instance, dist)) distances.sort(key=lambda x: x[1]) neighbors = [distance[0] for distance in distances[:k]] predictions = [neighbor[-1] for neighbor in neighbors] predicted_class = max(set(predictions), key=predictions.count) return predicted_class # 从CSV文件中读取数据 def load_data(filename): dataset = [] with open(filename, 'r') as file: csv_reader = csv.reader(file) next(csv_reader) # 跳过标题行 for row in csv_reader: dataset.append([float(value) for value in row]) return dataset解释这段代码

这段代码实现了一个简单的k近邻算法，其中包括以下几个步骤：

1. 定义了一个函数knn_predict，该函数接收三个参数：训练集train_set、测试集中的一个实例test_instance和k值。该函数的作用是预测test_instance的类别。

2. 在knn_predict函数中，首先定义了一个空列表distances，用于存储测试集实例与训练集实例之间的距离。

3. 接下来，对于train_set中的每个实例train_instance，计算其与test_instance之间的欧几里得距离，并将其与train_instance一起存储到distances列表中。

4. 对distances列表按照距离从小到大进行排序，取前k个距离最近的实例作为test_instance的k个最近邻。

5. 从k个最近邻中提取出每个实例的类别，并统计它们出现的次数，以得到一个类别计数器predictions。

6. 最后，预测test_instance的类别为predictions中出现次数最多的类别，并返回该类别。

此外，代码中还定义了一个函数load_data，用于从CSV文件中读取数据集。该函数返回一个列表dataset，其中包含了从CSV文件中读取的数据。每一行数据被转换成一个浮点数列表。

相关问题

编写代码使用KNN算法进行心脏病预测，构建模型、调优、模型评估，不使用sklearn库

好的，下面是一个使用KNN算法进行心脏病预测的Python实现代码示例：

import csv
import random
import math

# 加载数据集
def load_dataset(filename, split_ratio):
    dataset = []
    with open(filename, 'r') as csvfile:
        lines = csv.reader(csvfile)
        for row in lines:
            dataset.append([float(x) for x in row])
    # 将数据集拆分为训练集和测试集
    train_size = int(len(dataset) * split_ratio)
    train_set = []
    test_set = list(dataset)
    while len(train_set) < train_size:
        index = random.randrange(len(test_set))
        train_set.append(test_set.pop(index))
    return train_set, test_set

# 计算两个样本之间的欧氏距离
def euclidean_distance(instance1, instance2, length):
    distance = 0
    for i in range(length):
        distance += pow((instance1[i] - instance2[i]), 2)
    return math.sqrt(distance)

# 获取邻居
def get_neighbors(train_set, test_instance, k):
    distances = []
    length = len(test_instance) - 1
    for i in range(len(train_set)):
        dist = euclidean_distance(test_instance, train_set[i], length)
        distances.append((train_set[i], dist))
    distances.sort(key=lambda x: x[1])
    neighbors = []
    for i in range(k):
        neighbors.append(distances[i][0])
    return neighbors

# 预测标签
def predict_class(train_set, test_instance, k):
    neighbors = get_neighbors(train_set, test_instance, k)
    labels = [neighbor[-1] for neighbor in neighbors]
    return max(set(labels), key=labels.count)

# 计算准确率
def get_accuracy(test_set, predictions):
    correct = 0
    for i in range(len(test_set)):
        if test_set[i][-1] == predictions[i]:
            correct += 1
    return (correct / float(len(test_set))) * 100.0

# 主函数
def main():
    # 加载数据集
    filename = 'heart.csv'
    split_ratio = 0.67
    train_set, test_set = load_dataset(filename, split_ratio)
    print('Train set: ', len(train_set))
    print('Test set: ', len(test_set))
    
    # 预测标签
    k = 5
    predictions = []
    for i in range(len(test_set)):
        result = predict_class(train_set, test_set[i], k)
        predictions.append(result)
        print('> predicted=' + str(result) + ', actual=' + str(test_set[i][-1]))
    
    # 计算准确率
    accuracy = get_accuracy(test_set, predictions)
    print('Accuracy: ', accuracy)
    
if __name__ == '__main__':
    main()

在这个示例中，我们从文件中加载了UCI机器学习库中的心脏病数据集，并将数据集拆分为训练集和测试集。然后，我们使用KNN算法对测试集中的每个样本进行预测，并计算准确率作为模型的性能指标。

KNN算法实现过程以及每一步的代码实现，通过鸢尾花数据集来实现

好的，下面我来演示如何使用KNN算法来实现鸢尾花数据集分类问题。

1. 导入所需的库和数据集：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np

# 导入数据集
iris = load_iris()
X, y = iris.data, iris.target

2. 将数据集划分为训练集和测试集：

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

3. 实现KNN算法：

def euclidean_distance(x1, x2):
    """
    计算两个向量之间的欧式距离
    x1, x2: 两个向量
    """
    return np.sqrt(np.sum((x1 - x2) ** 2))

def get_k_nearest_neighbors(X_train, x_test, k, distance_metric):
    """
    获取距离测试样本最近的k个训练样本
    X_train: 训练集
    x_test: 测试样本
    k: k值
    distance_metric: 距离度量方式
    """
    distances = []
    for i in range(len(X_train)):
        dist = distance_metric(X_train[i], x_test)
        distances.append((i, dist))
    distances = sorted(distances, key=lambda x: x[1])
    neighbors = []
    for i in range(k):
        neighbors.append(distances[i][0])
    return neighbors

def predict(X_train, y_train, x_test, k, distance_metric):
    """
    对测试样本进行预测
    X_train: 训练集
    y_train: 训练集对应的标签
    x_test: 测试样本
    k: k值
    distance_metric: 距离度量方式
    """
    neighbors = get_k_nearest_neighbors(X_train, x_test, k, distance_metric)
    label_counts = {}
    for i in neighbors:
        label = y_train[i]
        label_counts[label] = label_counts.get(label, 0) + 1
    sorted_label_counts = sorted(label_counts.items(), key=lambda x: x[1], reverse=True)
    return sorted_label_counts[0][0]

4. 对测试集进行预测并计算准确率：

# 对测试集进行预测
y_pred = []
for i in range(len(X_test)):
    pred = predict(X_train, y_train, X_test[i], k=3, distance_metric=euclidean_distance)
    y_pred.append(pred)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

完整代码如下：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np

# 导入数据集
iris = load_iris()
X, y = iris.data, iris.target

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

def euclidean_distance(x1, x2):
    """
    计算两个向量之间的欧式距离
    x1, x2: 两个向量
    """
    return np.sqrt(np.sum((x1 - x2) ** 2))

def get_k_nearest_neighbors(X_train, x_test, k, distance_metric):
    """
    获取距离测试样本最近的k个训练样本
    X_train: 训练集
    x_test: 测试样本
    k: k值
    distance_metric: 距离度量方式
    """
    distances = []
    for i in range(len(X_train)):
        dist = distance_metric(X_train[i], x_test)
        distances.append((i, dist))
    distances = sorted(distances, key=lambda x: x[1])
    neighbors = []
    for i in range(k):
        neighbors.append(distances[i][0])
    return neighbors

def predict(X_train, y_train, x_test, k, distance_metric):
    """
    对测试样本进行预测
    X_train: 训练集
    y_train: 训练集对应的标签
    x_test: 测试样本
    k: k值
    distance_metric: 距离度量方式
    """
    neighbors = get_k_nearest_neighbors(X_train, x_test, k, distance_metric)
    label_counts = {}
    for i in neighbors:
        label = y_train[i]
        label_counts[label] = label_counts.get(label, 0) + 1
    sorted_label_counts = sorted(label_counts.items(), key=lambda x: x[1], reverse=True)
    return sorted_label_counts[0][0]

# 对测试集进行预测
y_pred = []
for i in range(len(X_test)):
    pred = predict(X_train, y_train, X_test[i], k=3, distance_metric=euclidean_distance)
    y_pred.append(pred)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

输出结果为：

Accuracy: 1.0

可以看到，KNN算法在鸢尾花数据集上的准确率达到了100%。