六、对iris数据集进行拆分，并查看拆分结果。 (1)正确调用鸢尾花iris数据集 (2)正确调用sklearn.model_selection类中的train_test_split函数 (3)正确使用train_test_split函数拆分数据集 (4)输出X_train和X_test的维度

以下是对iris数据集进行拆分的代码示例：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载iris数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将数据集拆分为训练集和测试集，其中测试集占比为30%
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# 输出X_train和X_test的维度
print("X_train shape:", X_train.shape)  # (105, 4)
print("X_test shape:", X_test.shape)    # (45, 4)

在上面的代码中，我们首先调用sklearn.datasets库中的load_iris函数加载iris数据集，并将其赋值给X和y变量。然后，我们使用sklearn.model_selection库中的train_test_split函数将数据集拆分为训练集和测试集，其中测试集占比为30%。最后，我们输出X_train和X_test的维度，即训练集和测试集的样本数量和特征数量。

相关问题

准备：鸢尾花数据集。 调用sklearn库，使用KNN分类方法完成鸢尾花数据的分类。

好的，我可以帮你完成这个任务。首先，我们需要先导入鸢尾花数据集并将其拆分为训练集和测试集。然后，我们可以使用sklearn库中的KNeighborsClassifier方法来训练一个KNN模型，并用测试集评估其性能。下面是完整的代码：

# 导入必要的库
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 加载鸢尾花数据集
iris = load_iris()

# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# 创建KNN分类器并训练模型
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)

# 使用测试集评估模型性能
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print('Accuracy:', accuracy)

输出结果应该类似于：Accuracy: 1.0，这表示我们的KNN模型在鸢尾花数据集上取得了100%的准确率。

给出python实现贝叶斯算法的代码，不调用sklearn库，对鸢尾花数据集分类，实现结果可视化

好的，以下是贝叶斯算法的python代码（不使用sklearn库）：

import csv
import random
import math
import numpy as np
import matplotlib.pyplot as plt

def load_dataset(filename):
    dataset = []
    with open(filename, 'r') as csv_file:
        csv_reader = csv.reader(csv_file)
        for row in csv_reader:
            if not row:
                continue
            data = [float(x) for x in row[:-1]]
            label = row[-1]
            dataset.append((data, label))
    return dataset

def split_dataset(dataset, split_ratio):
    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

def separate_by_class(dataset):
    separated = {}
    for data, label in dataset:
        if label not in separated:
            separated[label] = []
        separated[label].append(data)
    return separated

def calculate_mean(numbers):
    return sum(numbers) / float(len(numbers))

def calculate_stddev(numbers):
    mean = calculate_mean(numbers)
    variance = sum([pow(x - mean, 2) for x in numbers]) / float(len(numbers) - 1)
    return math.sqrt(variance)

def summarize_dataset(dataset):
    summaries = [(calculate_mean(attribute), calculate_stddev(attribute)) for attribute in zip(*dataset)]
    del summaries[-1]
    return summaries

def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = {}
    for class_label, instances in separated.items():
        summaries[class_label] = summarize_dataset(instances)
    return summaries

def calculate_probability(x, mean, stddev):
    exponent = math.exp(-math.pow(x - mean, 2) / (2 * math.pow(stddev, 2)))
    return (1 / (math.sqrt(2 * math.pi) * stddev)) * exponent

def calculate_class_probabilities(summaries, input_vector):
    probabilities = {}
    for class_label, class_summaries in summaries.items():
        probabilities[class_label] = 1
        for i in range(len(class_summaries)):
            mean, stddev = class_summaries[i]
            x = input_vector[i]
            probabilities[class_label] *= calculate_probability(x, mean, stddev)
    return probabilities

def predict(summaries, input_vector):
    probabilities = calculate_class_probabilities(summaries, input_vector)
    best_label, best_prob = None, -1
    for class_label, probability in probabilities.items():
        if best_label is None or probability > best_prob:
            best_prob = probability
            best_label = class_label
    return best_label

def get_predictions(summaries, test_set):
    predictions = []
    for i in range(len(test_set)):
        result = predict(summaries, test_set[i][0])
        predictions.append(result)
    return predictions

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

def plot_data(test_data, predicted_data):
    data = np.array(test_data)
    colors = ['blue', 'red', 'green']
    markers = ['o', 's', '^']
    for i in range(3):
        x = data[data[:, -1] == str(i)][:, 0]
        y = data[data[:, -1] == str(i)][:, 1]
        predicted_x = data[predicted_data == str(i)][:, 0]
        predicted_y = data[predicted_data == str(i)][:, 1]
        plt.scatter(x, y, color=colors[i], marker=markers[i], label=f'Class {i}')
        plt.scatter(predicted_x, predicted_y, edgecolors='black', facecolors='none', marker=markers[i])
    plt.legend()
    plt.show()

if __name__ == '__main__':
    filename = 'iris.csv'
    split_ratio = 0.67
    dataset = load_dataset(filename)
    train_set, test_set = split_dataset(dataset, split_ratio)
    print(f'Split {len(dataset)} rows into train={len(train_set)} and test={len(test_set)} rows')
    summaries = summarize_by_class(train_set)
    predictions = get_predictions(summaries, test_set)
    accuracy = get_accuracy(test_set, predictions)
    print(f'Accuracy: {accuracy}%')
    plot_data(test_set, np.array(predictions))

其中，`filename` 变量指定数据集文件名；`split_ratio` 变量指定数据集的拆分比例；`load_dataset` 函数从 CSV 文件读取数据集；`split_dataset` 函数将数据集拆分成训练集和测试集；`separate_by_class` 函数将数据集按标签分类；`summarize_dataset` 函数计算数据集每个特征的均值和标准差，返回这些统计数据作为一个元组数组；`summarize_by_class` 函数计算每个类别的数据集的统计数据；`calculate_probability` 函数计算特征值的概率密度函数值；`calculate_class_probabilities` 函数计算输入向量的每个类别的概率密度函数值；`predict` 函数基于概率密度函数值选择可能性最高的类别作为预测结果；`get_predictions` 函数计算测试数据集的所有预测结果；`get_accuracy` 函数计算预测结果的准确率；`plot_data` 函数将测试数据集和预测结果可视化。