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数据挖掘算法源码

来源:在心算法网 2024-07-11 08:33:54

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数据挖掘算法源码(1)

数据挖掘是一种从大量数据中自动发现模式、提取知识的方法,它在各个领域都有广泛的应用在+心+算+法+网。数据挖掘算法是实现数据挖掘的关,下面介绍几种常用的数据挖掘算法及其源码

1. K-Means算法

  K-Means算法是一种聚类算法,它将n个数据对象分成k个簇,使簇内的对象相似度较高,簇的相似度较低。K-Means算法的核心思想是:随机选择k个点作为初始的聚类中心,然个数据对象分配与其最近的聚类中心所在的簇中,重新计算个簇的聚类中心,直聚类中心再发生变化或达预定的迭代次数hDY。K-Means算法的源码如下:

  ```python

import numpy as np

  def kmeans(X, k, max_iter=100):

  m, n = X.shape

centroids = X[np.random.choice(m, k, replace=False)]

  for i in range(max_iter):

C = np.argmin(np.sum((X[:, np.newaxis, :] - centroids)**2, axis=2), axis=1)

for j in range(k):

  centroids[j] = np.mean(X[C == j], axis=0)

  return centroids, C

  ```

数据挖掘算法源码(2)

2. Apriori算法

  Apriori算法是一种频繁模式挖掘算法,它用于发现数据集中的频繁项集。Apriori算法的核心思想是:如果一个项集是频繁的,那么它的所有子集也一定是频繁的。Apriori算法的步骤如下:首先找出所有个项的频繁项集,然利用频繁项集生成候选项集,再根据候选项集计算支持度,筛选出频繁项集,重复上述步骤直能再生成新的频繁项集为止在_心_算_法_网。Apriori算法的源码如下:

  ```python

  def apriori(data, min_support=0.5):

  itemsets = {}

transaction_list = [set(transaction) for transaction in data]

  itemsets[1] = {frozenset([item]): transaction_list.count({item}) / len(transaction_list) for item in set(item for transaction in transaction_list for item in transaction)}

  k = 2

while itemsets[k-1]:

  itemsets[k] = {}

candidates = set([item for itemset in itemsets[k-1] for item in itemset])

  for candidate in [frozenset([item]) for item in candidates]:

itemsets[k][candidate] = sum(1 for transaction in transaction_list if candidate.issubset(transaction)) / len(transaction_list)

  itemsets[k] = {itemset: support for itemset, support in itemsets[k].items() if support >= min_support}

  k += 1

  return itemsets

  ```

3. 决策树算法

决策树算法是一种分类算法,它通过构建一棵树来对数据进行分类。决策树算法的核心思想是:选择最优特征进行划分,使个子节点的纯度最高。决策树算法的步骤如下:首先选择最优特征作为根节点,然根据该特征将数据集划分成多个子集,对个子集递归地构建决策树,直满足停止条件在.心.算.法.网。决策树算法的源码如下:

  ```python

  class DecisionTree:

def __init__(self, max_depth=None, min_samples_split=2, min_samples_leaf=1):

  self.max_depth = max_depth

  self.min_samples_split = min_samples_split

self.min_samples_leaf = min_samples_leaf

  self.tree = {}

def fit(self, X, y):

  self.tree = self._build_tree(X, y)

def predict(self, X):

return [self._predict(x, self.tree) for x in X]

  def _build_tree(self, X, y, depth=0):

  n_samples, n_features = X.shape

n_labels = len(set(y))

  if depth == self.max_depth or n_samples < self.min_samples_split or n_labels == 1:

return Counter(y).most_common(1)[0][0]

  best_feature, best_threshold = self._find_best_split(X, y, n_features)

  left_indices = X[:, best_feature] < best_threshold

right_indices = X[:, best_feature] >= best_threshold

left_tree = self._build_tree(X[left_indices], y[left_indices], depth+1)

  right_tree = self._build_tree(X[right_indices], y[right_indices], depth+1)

  return {'feature': best_feature,

  'threshold': best_threshold,

  'left_tree': left_tree,

  'right_tree': right_tree}

  def _find_best_split(self, X, y, n_features):

  best_feature, best_threshold = None, None

  best_impurity = 1

  for feature in range(n_features):

  thresholds = sorted(set(X[:, feature]))

  for i in range(1, len(thresholds)):

threshold = (thresholds[i-1] + thresholds[i]) / 2

  y_left = y[X[:, feature] < threshold]

  y_right = y[X[:, feature] >= threshold]

  impurity = self._calculate_impurity(y_left, y_right)

  if impurity < best_impurity:

  best_feature = feature

  best_threshold = threshold

  best_impurity = impurity

  return best_feature, best_threshold

def _calculate_impurity(self, y_left, y_right):

  n_left, n_right = len(y_left), len(y_right)

  impurity = 0

for y in [y_left, y_right]:

  if len(y) == 0:

continue

  counts = Counter(y)

  probs = [count / len(y) for count in counts.values()]

  impurity += sum([-prob * np.log2(prob) for prob in probs])

return impurity * (n_left / (n_left + n_right))

  def _predict(self, x, tree):

  if isinstance(tree, int):

  return tree

feature, threshold, left_tree, right_tree = tree['feature'], tree['threshold'], tree['left_tree'], tree['right_tree']

  if x[feature] < threshold:

  return self._predict(x, left_tree)

else:

return self._predict(x, right_tree)

  ```

4. 支持向量机算法

  支持向量机算法是一种分类算法,它通过构建一个超平面来对数据进行分类。支持向量机算法的核心思想是:找一个最优的超平面,使该超平面最近的数据点该超平面的距最大。支持向量机算法的步骤如下:首先选择一个核函数,将数据映射高维空,然在高维空中找一个最优的超平面,使该超平面最近的数据点该超平面的距最大www.minaka66.net。支持向量机算法的源码如下:

  ```python

class SVM:

def __init__(self, C=1.0, kernel='linear', degree=3, gamma='scale'):

self.C = C

  self.kernel = kernel

  self.degree = degree

  self.gamma = gamma

  self.alpha = None

self.b = None

def fit(self, X, y):

  n_samples, n_features = X.shape

K = self._kernel(X, X)

  P = cvxopt.matrix(np.outer(y, y) * K)

q = cvxopt.matrix(-np.ones(n_samples))

  G = cvxopt.matrix(np.vstack((-np.eye(n_samples), np.eye(n_samples))))

  h = cvxopt.matrix(np.hstack((np.zeros(n_samples), np.ones(n_samples) * self.C)))

A = cvxopt.matrix(y.reshape(1, -1))

  b = cvxopt.matrix(np.zeros(1))

solution = cvxopt.solvers.qp(P, q, G, h, A, b)

  self.alpha = np.ravel(solution['x'])

  support_vectors = self.alpha > 1e-5

self.X = X[support_vectors]

  self.y = y[support_vectors]

self.alpha = self.alpha[support_vectors]

self.b = np.mean(self.y - np.sum(self.alpha * self.y * K[support_vectors, :], axis=0))

def predict(self, X):

  K = self._kernel(X, self.X)

  return np.sign(np.sum(self.alpha * self.y * K, axis=1) + self.b)

  def _kernel(self, X1, X2):

  if self.kernel == 'linear':

  return np.dot(X1, X2.T)

elif self.kernel == 'poly':

  return (np.dot(X1, X2.T) + 1) ** self.degree

  elif self.kernel == 'rbf':

  return np.exp(-self.gamma * np.sum((X1[:, np.newaxis, :] - X2[np.newaxis, :, :]) ** 2, axis=-1))

  ```

数据挖掘算法源码(3)

总结

  本文介绍了几种常用的数据挖掘算法及其源码,包括K-Means算法、Apriori算法、决策树算法和支持向量机算法。些算法在各个领域都有广泛的应用,对于数据挖掘的初学者来说,掌握些算法的原理和实现方法是非常重要的。

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