花的种类。TXT文件中五列数据分别对应花萼长度、花萼宽度、花瓣长度、花瓣宽度和种类，其中种类分别为山鸢尾Iris-setosa、变色鸢尾Iris-versicolor和维吉尼亚鸢尾Iris-virginica三个类别。 根据使用KNN 算法进行花的种类对数据再进行分类 （15分）每一种类的花前面80%数据作为训练数据，后面20%数据作为预测数据，计算出预测准确率

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# 使用 loadtxt() 函数读取 txt 文件，获取花的数据
with open('花的种类.txt', 'r', encoding='utf-8') as f:
data = np.loadtxt(f, delimiter=',', usecols=[0, 1, 2, 3], dtype=float, skiprows=1)
labels = np.loadtxt(f, delimiter=',', usecols=[4], dtype=str, skiprows=1)

# 划分训练集和测试集
train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.2)

# 训练模型
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(train_data, train_labels)

# 预测结果
pred_labels = knn.predict(test_data)

# 计算准确率
accuracy = np.mean(pred_labels == test_labels)
print('预测准确率：', accuracy)

相关问题

使用python编写以下程序：Flower.txt文件中五列数据分别对应花萼长度、花萼宽度、花瓣长度、花瓣宽度和种类。读取其中全部花萼长度数据，使用Numpy并对花萼长度数据进行排序、去重，并求出和、累积和、均值、标准差、方差、最小值、最大值。

以下是使用Python编写的程序，实现了对花萼长度数据的处理：

import numpy as np

# 读取文件数据
data = np.genfromtxt('Flower.txt', delimiter=',', dtype=str)

# 提取花萼长度数据
sepal_lengths = data[:, 0].astype(float)

# 排序并去重
sorted_lengths = np.sort(np.unique(sepal_lengths))

# 求和、累积和、均值、标准差、方差、最小值、最大值
sum_lengths = np.sum(sepal_lengths)
cumsum_lengths = np.cumsum(sorted_lengths)
mean_lengths = np.mean(sorted_lengths)
std_lengths = np.std(sorted_lengths)
var_lengths = np.var(sorted_lengths)
min_length = np.min(sorted_lengths)
max_length = np.max(sorted_lengths)

# 输出结果
print("Sorted and unique sepal lengths:", sorted_lengths)
print("Sum:", sum_lengths)
print("Cumulative sum:", cumsum_lengths)
print("Mean:", mean_lengths)
print("Standard deviation:", std_lengths)
print("Variance:", var_lengths)
print("Minimum:", min_length)
print("Maximum:", max_length)

请确保将数据文件 `Flower.txt` 与 Python 程序放在同一目录下，并且安装了 NumPy 库。运行程序后，将输出花萼长度数据的排序结果，以及求和、累积和、均值、标准差、方差、最小值和最大值等统计信息。

使用python编写以下程序：Flower.txt文件中五列数据分别对应花萼长度、花萼宽度、花瓣长度、花瓣宽度和种类，其中种类分别为Iris-setosa、Iris-versicolor和Iris-virginica三个类别。按种类读取其中的的花萼长度数据，使用Numpy并对每个种类的花萼长度数据进行排序、去重，并求出和、累积和、均值、标准差、方差、最小值、最大值。

以下是使用Python编写的程序，实现了对花萼长度数据的处理：

import numpy as np

# 读取文件数据
data = np.genfromtxt('Flower.txt', delimiter=',', dtype=str)

# 提取花萼长度数据
sepal_lengths = data[:, 0].astype(float)

# 按种类分类
setosa_lengths = sepal_lengths[data[:, 4] == 'Iris-setosa']
versicolor_lengths = sepal_lengths[data[:, 4] == 'Iris-versicolor']
virginica_lengths = sepal_lengths[data[:, 4] == 'Iris-virginica']

# 排序并去重
setosa_sorted = np.sort(np.unique(setosa_lengths))
versicolor_sorted = np.sort(np.unique(versicolor_lengths))
virginica_sorted = np.sort(np.unique(virginica_lengths))

# 求和、累积和、均值、标准差、方差、最小值、最大值
setosa_sum = np.sum(setosa_lengths)
versicolor_sum = np.sum(versicolor_lengths)
virginica_sum = np.sum(virginica_lengths)

setosa_cumsum = np.cumsum(setosa_sorted)
versicolor_cumsum = np.cumsum(versicolor_sorted)
virginica_cumsum = np.cumsum(virginica_sorted)

setosa_mean = np.mean(setosa_sorted)
versicolor_mean = np.mean(versicolor_sorted)
virginica_mean = np.mean(virginica_sorted)

setosa_std = np.std(setosa_sorted)
versicolor_std = np.std(versicolor_sorted)
virginica_std = np.std(virginica_sorted)

setosa_var = np.var(setosa_sorted)
versicolor_var = np.var(versicolor_sorted)
virginica_var = np.var(virginica_sorted)

setosa_min = np.min(setosa_sorted)
versicolor_min = np.min(versicolor_sorted)
virginica_min = np.min(virginica_sorted)

setosa_max = np.max(setosa_sorted)
versicolor_max = np.max(versicolor_sorted)
virginica_max = np.max(virginica_sorted)

# 输出结果
print("Iris-setosa:")
print("Sorted and unique sepal lengths:", setosa_sorted)
print("Sum:", setosa_sum)
print("Cumulative sum:", setosa_cumsum)
print("Mean:", setosa_mean)
print("Standard deviation:", setosa_std)
print("Variance:", setosa_var)
print("Minimum:", setosa_min)
print("Maximum:", setosa_max)

print("\nIris-versicolor:")
print("Sorted and unique sepal lengths:", versicolor_sorted)
print("Sum:", versicolor_sum)
print("Cumulative sum:", versicolor_cumsum)
print("Mean:", versicolor_mean)
print("Standard deviation:", versicolor_std)
print("Variance:", versicolor_var)
print("Minimum:", versicolor_min)
print("Maximum:", versicolor_max)

print("\nIris-virginica:")
print("Sorted and unique sepal lengths:", virginica_sorted)
print("Sum:", virginica_sum)
print("Cumulative sum:", virginica_cumsum)
print("Mean:", virginica_mean)
print("Standard deviation:", virginica_std)
print("Variance:", virginica_var)
print("Minimum:", virginica_min)
print("Maximum:", virginica_max)

请确保将数据文件 `Flower.txt` 与 Python 程序放在同一目录下，并且安装了 NumPy 库。运行程序后，将输出每个种类的花萼长度数据的排序结果，以及求和、累积和、均值、标准差、方差、最小值和最大值等统计信息。