python编写的动物识别专家系统_基于Python的动物识别专家系统

Python编写的动物识别专家系统是一种基于人工智能技术的应用程序，它可以通过对动物特征进行分析和比对，快速准确的识别出动物的种类。

下面是一份基于Python的动物识别专家系统的简单实现：

# 导入相关库
import numpy as np
import pandas as pd

# 读取数据集
data = pd.read_csv('animal.csv')

# 将数据集分为训练集和测试集
train_data = data.iloc[:80,:]
test_data = data.iloc[80:,:]

# 定义决策树节点类
class Node:
    def __init__(self, feature=None, value=None, left=None, right=None, label=None):
        self.feature = feature  # 特征列索引
        self.value = value      # 划分阈值
        self.left = left        # 左子树
        self.right = right      # 右子树
        self.label = label      # 叶子节点类别标签

# 计算信息熵
def entropy(labels):
    n = len(labels)
    if n == 0:
        return 0
    counts = np.bincount(labels)
    probs = counts[np.nonzero(counts)] / n
    return -np.sum(probs * np.log2(probs))

# 计算信息增益
def information_gain(X, y, feature, threshold):
    left_indices = X[:, feature] < threshold
    right_indices = X[:, feature] >= threshold
    left_labels = y[left_indices]
    right_labels = y[right_indices]
    if len(left_labels) == 0 or len(right_labels) == 0:
        return 0
    parent_entropy = entropy(y)
    left_entropy = entropy(left_labels)
    right_entropy = entropy(right_labels)
    child_entropy = (len(left_labels) / len(y)) * left_entropy + \
                    (len(right_labels) / len(y)) * right_entropy
    return parent_entropy - child_entropy

# ID3算法
def id3(X, y):
    # 如果所有样本都属于同一类别，返回叶子节点
    if len(np.unique(y)) == 1:
        return Node(label=y[0])
    # 如果没有可用特征，返回叶子节点
    if X.shape[1] == 0:
        return Node(label=np.bincount(y).argmax())
    # 计算每个特征的信息增益
    gains = [information_gain(X, y, feature, threshold)
             for feature in range(X.shape[1])
             for threshold in np.unique(X[:, feature])]
    best_feature, best_threshold = np.unravel_index(np.argmax(gains), X.shape[:2])
    # 如果信息增益为0，返回叶子节点
    if gains[best_feature, best_threshold] == 0:
        return Node(label=np.bincount(y).argmax())
    # 构建决策树
    left_indices = X[:, best_feature] < best_threshold
    right_indices = X[:, best_feature] >= best_threshold
    left_subtree = id3(X[left_indices], y[left_indices])
    right_subtree = id3(X[right_indices], y[right_indices])
    return Node(feature=best_feature, value=best_threshold, left=left_subtree, right=right_subtree)

# 预测
def predict(root, x):
    if root.label is not None:
        return root.label
    subtree = root.left if x[root.feature] < root.value else root.right
    return predict(subtree, x)

# 训练模型
X_train = train_data.iloc[:, :-1].values
y_train = train_data.iloc[:, -1].values
root = id3(X_train, y_train)

# 预测测试集
X_test = test_data.iloc[:, :-1].values
y_test = test_data.iloc[:, -1].values
y_pred = np.array([predict(root, x) for x in X_test])

# 输出准确率
accuracy = np.mean(y_pred == y_test)
print('Accuracy:', accuracy)

在该实现中，我们使用了ID3算法来构建决策树，并使用了信息熵和信息增益来进行特征选择。通过对训练集和测试集的预测，我们可以得到该动物识别专家系统的准确率。