本文整理汇总了Python中sklearn.neighbors.LSHForest.set_params方法的典型用法代码示例。如果您正苦于以下问题：Python LSHForest.set_params方法的具体用法？Python LSHForest.set_params怎么用？Python LSHForest.set_params使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neighbors.LSHForest的用法示例。
在下文中一共展示了LSHForest.set_params方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: SMOTE
# 需要导入模块: from sklearn.neighbors import LSHForest [as 别名]
# 或者: from sklearn.neighbors.LSHForest import set_params [as 别名]
#.........这里部分代码省略.........
y_resampled = np.concatenate((y, y_new), axis=0)
return X_resampled, y_resampled
if self.kind == 'borderline1' or self.kind == 'borderline2':
if self.verbose:
print("Finding the {} nearest neighbours...".format(self.m))
# Find the NNs for all samples in the data set.
self.nearest_neighbour_.fit(X)
if self.verbose:
print("done!")
# Boolean array with True for minority samples in danger
danger_index = self._in_danger_noise(X_min, y, kind='danger')
# If all minority samples are safe, return the original data set.
if not any(danger_index):
if self.verbose:
print('There are no samples in danger. No borderline '
'synthetic samples created.')
# All are safe, nothing to be done here.
return X, y
# If we got here is because some samples are in danger, we need to
# find the NNs among the minority class to create the new synthetic
# samples.
#
# We start by changing the number of NNs to consider from m + 1
# to k + 1
self.nearest_neighbour_.set_params(**{'n_neighbors': self.k + 1})
self.nearest_neighbour_.fit(X_min)
# nns...#
nns = self.nearest_neighbour_.kneighbors(
X_min[danger_index],
return_distance=False)[:, 1:]
# B1 and B2 types diverge here!!!
if self.kind == 'borderline1':
# Create synthetic samples for borderline points.
X_new, y_new = self._make_samples(X_min[danger_index],
self.min_c_,
X_min,
nns,
num_samples)
# Concatenate the newly generated samples to the original
# dataset
X_resampled = np.concatenate((X, X_new), axis=0)
y_resampled = np.concatenate((y, y_new), axis=0)
# Reset the k-neighbours to m+1 neighbours
self.nearest_neighbour_.set_params(**{'n_neighbors': self.m+1})
return X_resampled, y_resampled
else:
# Split the number of synthetic samples between only minority
# (type 1), or minority and majority (with reduced step size)
# (type 2).
np.random.seed(self.rs_)