不调用sklearn实现葡萄酒分类

好的，我可以帮您实现葡萄酒分类。为了方便起见，我将使用Python语言和NumPy库来实现算法。首先，我们需要导入数据集并对其进行预处理。葡萄酒数据集可以在scikit-learn库中找到，我们可以使用它来训练和测试我们的模型。下面是代码示例：

from sklearn.datasets import load_wine
import numpy as np

wine = load_wine()

X = wine.data
y = wine.target

# 将数据集分为训练集和测试集
indices = np.random.permutation(len(X))
train_indices, test_indices = indices[:130], indices[130:]
X_train, X_test = X[train_indices], X[test_indices]
y_train, y_test = y[train_indices], y[test_indices]

接下来，我们可以定义一个简单的KNN分类器来对葡萄酒数据进行分类。KNN算法是一种非常简单的分类算法，它根据最近邻居的标签来预测新样本的标签。下面是代码示例：

from collections import Counter

def euclidean_distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2)**2))

class KNN:
    def __init__(self, k=3):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return np.array(y_pred)

    def _predict(self, x):
        # 计算距离
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
        # 获取k个最近邻的标签
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]
        # 选择最常见的标签作为预测标签
        most_common = Counter(k_nearest_labels).most_common(1)
        return most_common[0][0]

最后，我们可以使用训练集来训练我们的模型，并使用测试集来测试模型的准确性。下面是代码示例：

knn = KNN(k=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)

accuracy = np.sum(y_pred == y_test) / len(y_test)
print(f'Accuracy: {accuracy}')

这就是使用Python和NumPy实现葡萄酒分类的简单算法。希望对您有所帮助！