用iris数据做最大熵模型的代码

以下是使用最大熵模型对iris数据集进行分类的Python代码：

from sklearn.datasets import load_iris
from sklearn.feature_extraction.text import CountVectorizer
from collections import defaultdict
import numpy as np

# 加载iris数据集
iris = load_iris()

# 特征提取，将iris数据集中的特征转化为特征向量
X = iris.data
y = iris.target
vectorizer = CountVectorizer()
feature_vectors = vectorizer.fit_transform(X)

# 构建最大熵模型
class MaxEnt(object):
    def __init__(self, EPS=0.005):
        self._EPS = EPS
        self._samples = []
        self._Y = set([])  
        self._numXY = defaultdict(int)  
        self._N = 0  
        self._ne = set([]) 
        self._xyID = {}
        self._n = 0

    def LoadData(self, data):
        self._samples = data
        for items in self._samples:
            Y = items[0]
            X = items[1:]
            self._Y.add(Y)
            for x in set(X):
                self._ne.add((Y, x))
                self._numXY[(Y, x)] += 1

    def _Initparams(self):
        self._n = len(self._samples)
        self._N = float(len(self._samples))
        for i, y in enumerate(self._Y):
            self._xyID[y] = i
        self._w = [0.0] * self._n
        self._lastw = self._w[:]

    def probwgt(self, features, label):
        wgt = 0.0
        for f in features:
            if (label, f) in self._ne:
                wgt += self._w[self._xyID[(label, f)]]
        return np.exp(wgt)

    def _Convergence(self):
        for last, now in zip(self._lastw, self._w):
            if abs(last - now) >= self._EPS:
                return False
        return True

    def train(self, maxiter=1000):
        self._Initparams()
        for i in range(maxiter):
            print('iter:%d' % i)
            self._lastw = self._w[:]
            iterloss = 0
            for j, sw in enumerate(self._samples):
                sx = sw[1:]
                sy = sw[0]
                ey = self._CalProb(sx)
                for y in self._Y:
                    pyx = self.probwgt(sx, y) / ey
                    if (y, sx) in self._numXY:
                        self._w[self._xyID[(y, sx)]] += 1.0 / self._N * np.log(pyx)
                    for x in sx:
                        if (y, x) in self._numXY:
                            self._w[self._xyID[(y, x)]] += 1.0 / self._N * np.log(pyx)
                loss = np.log(ey)
                for y in self._Y:
                    loss -= self.probwgt(sx, y) / ey * np.log(self.probwgt(sx, y) / ey)
                iterloss += loss
            print('iterloss:%f' % iterloss)
            if self._Convergence():
                break

    def _CalProb(self, features):
        wgs = [self.probwgt(features, y) for y in self._Y]
        Z = sum(wgs)
        return Z

    def predict(self, input):
        features = input
        wgs = [self.probwgt(features, y) for y in self._Y]
        Z = sum(wgs)
        prob = [w / Z for w in wgs]
        return prob

# 构建分类器
classifier = MaxEnt()
classifier.LoadData(np.c_[y, feature_vectors.toarray()])
classifier.train()

# 对测试样本进行分类
test_data = [[5.1, 3.5, 1.4, 0.2], [5.9, 3.0, 5.1, 1.8], [6.0, 3.4, 4.5, 1.6]]
test_vectors = vectorizer.transform(test_data)
for i, data in enumerate(test_data):
    prob = classifier.predict(test_vectors.toarray()[i])
    print(data, iris.target_names[np.argmax(prob)])

需要注意的是，使用最大熵模型对数据集进行分类需要对数据进行特征提取，并将数据转化为特征向量。在本代码中，使用了sklearn库中的`CountVectorizer`对数据进行特征提取，将每个特征转化为一个整数值。同时，在MaxEnt类的`LoadData`函数中，通过将目标值和特征向量组合成一个元素，将数据集按照最大熵模型的要求转化。