路径机器：一种算法思想

class PathMachine(BaseEstimator): def __init__(self, norm=np.linalg.norm, classify=False): self.norm = norm self.classify = classify def find_start(self, X): index_max = None value_max = -np.inf for index, x in enumerate(X): value = self.norm(x) if value > value_max: index_max = index value_max = value return index_max def find_next(self, point, target, X, y): index_min = None value_min = np.inf for index, x in enumerate(X): if self.classify and (y[index] != target): continue value = self.norm(x - point) if value < value_min: index_min = index value_min = value return index_min def fit(self, X, y): X = np.copy(X) y = np.copy(y).flatten() self.paths = {} if self.classify else [] start_index = self.find_start(X) start_value = X[start_index] start_target = y[start_index] X = np.delete(X, start_index, axis=0) y = np.delete(y, start_index, axis=0) while len(X) > 0: next_index = self.find_next(start_value, start_target, X, y) if self.classify and next_index is None: start_index = self.find_start(X) start_value = X[start_index] start_target = y[start_index] continue next_target = y[next_index] if self.classify: if not next_target in self.paths: self.paths[next_target] = [] self.paths[next_target].append({ 'start': start_value, 'next': X[next_index] }) else: self.paths.append({ 'start': start_value, 'next': X[next_index], 'value': start_target, 'target': next_target }) start_value = X[next_index] start_target = y[next_index] X = np.delete(X, next_index, axis=0) y = np.delete(y, next_index, axis=0) if self.classify: self.paths[start_target].append({ 'start': start_value, 'next': self.paths[start_target][0]['start'] }) else: self.paths.append({ 'start': start_value, 'next': self.paths[0]['start'], 'value': start_target, 'target': self.paths[0]['target'] }) return self def predict(self, X): result = [] for x in X: if self.classify: predicted = None min_distance = np.inf for target in self.paths: for path in self.paths[target]: point = x - path['start'] line = path['next'] - path['start'] if np.allclose(self.norm(line), 0): continue direction = line / self.norm(line) product = np.dot(point, direction) projection = product * direction distance = self.norm(projection - point) if distance < min_distance: predicted = target min_distance = distance result.append(predicted) else: predicted = None min_distance = np.inf for path in self.paths: point = x - path['start'] line = path['next'] - path['start'] if np.allclose(self.norm(line), 0): continue direction = line / self.norm(line) product = np.dot(point, direction) projection = product * direction parameter = np.sign(product) * self.norm(projection) /\ self.norm(line) distance = self.norm(projection - point) if distance < min_distance: predicted = (1 - parameter) * path['value'] +\ parameter * path['target'] min_distance = distance result.append(predicted) return np.array(result) def score(self, X, y): if self.classify: return f1_score(y.flatten(), self.predict(X), average='micro') else: return r2_score(y.flatten(), self.predict(X))