本文整理汇总了Python中sklearn.decomposition.DictionaryLearning.inverse_transform方法的典型用法代码示例。如果您正苦于以下问题：Python DictionaryLearning.inverse_transform方法的具体用法？Python DictionaryLearning.inverse_transform怎么用？Python DictionaryLearning.inverse_transform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.decomposition.DictionaryLearning的用法示例。
在下文中一共展示了DictionaryLearning.inverse_transform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: SC
# 需要导入模块: from sklearn.decomposition import DictionaryLearning [as 别名]
# 或者: from sklearn.decomposition.DictionaryLearning import inverse_transform [as 别名]
class SC(object):
"""
Wrapper for sklearn package. Performs sparse coding
Sparse Coding, or Dictionary Learning has 5 methods:
- fit(waveforms)
update class instance with Sparse Coding fit
- fit_transform()
do what fit() does, but additionally return the projection onto new basis space
- inverse_transform(A)
inverses the decomposition, returns waveforms for an input A, using Z^\dagger
- get_basis()
returns the basis vectors Z^\dagger
- get_params()
returns metadata used for fits.
"""
def __init__(self, num_components=10,
catalog_name='unknown',
alpha = 0.001,
transform_alpha = 0.01,
max_iter = 2000,
tol = 1e-9,
n_jobs = 1,
verbose = True,
random_state = None):
self._decomposition = 'Sparse Coding'
self._num_components = num_components
self._catalog_name = catalog_name
self._alpha = alpha
self._transform_alpha = 0.001
self._n_jobs = n_jobs
self._random_state = random_state
self._DL = DictionaryLearning(n_components=self._num_components,
alpha = self._alpha,
transform_alpha = self._transform_alpha,
n_jobs = self._n_jobs,
verbose = verbose,
random_state = self._random_state)
def fit(self,waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._DL.fit(self._waveforms)
def fit_transform(self,waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._A = self._DL.fit_transform(self._waveforms)
return self._A
def inverse_transform(self,A):
# convert basis back to waveforms using fit
new_waveforms = self._DL.inverse_transform(A)
return new_waveforms
def get_params(self):
# TODO know what catalog was used! (include waveform metadata)
params = self._DL.get_params()
params['num_components'] = params.pop('n_components')
params['Decompositon'] = self._decomposition
return params
def get_basis(self):
""" Return the SPCA basis vectors (Z^\dagger)"""
return self._DL.components_