本文整理汇总了Python中sklearn.decomposition.IncrementalPCA.finish方法的典型用法代码示例。如果您正苦于以下问题：Python IncrementalPCA.finish方法的具体用法？Python IncrementalPCA.finish怎么用？Python IncrementalPCA.finish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.IncrementalPCA的用法示例。
在下文中一共展示了IncrementalPCA.finish方法的2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: decomposition
# 需要导入模块: from sklearn.decomposition import IncrementalPCA [as 别名]
# 或者: from sklearn.decomposition.IncrementalPCA import finish [as 别名]
#.........这里部分代码省略.........
_min, _max = da.compute(self.data.min(), self.data.max())
else:
_min, _max = bounds
self.data = (self.data - _min) / (_max - _min)
# LEARN
this_data = []
try:
for chunk in progressbar(
self._block_iterator(
flat_signal=True,
get=get,
signal_mask=signal_mask,
navigation_mask=navigation_mask),
total=nblocks,
leave=True,
desc='Learn'):
this_data.append(chunk)
if len(this_data) == num_chunks:
thedata = np.concatenate(this_data, axis=0)
method(thedata)
this_data = []
if len(this_data):
thedata = np.concatenate(this_data, axis=0)
method(thedata)
except KeyboardInterrupt:
pass
# GET ALREADY CALCULATED RESULTS
if algorithm == 'PCA':
explained_variance = obj.explained_variance_
explained_variance_ratio = obj.explained_variance_ratio_
factors = obj.components_.T
elif algorithm == 'ORPCA':
_, _, U, S, V = obj.finish()
factors = U * S
loadings = V
explained_variance = S**2 / len(factors)
elif algorithm == 'ONMF':
factors, loadings = obj.finish()
loadings = loadings.T
# REPROJECT
if reproject:
if algorithm == 'PCA':
method = obj.transform
post = lambda a: np.concatenate(a, axis=0)
elif algorithm == 'ORPCA':
method = obj.project
obj.R = []
post = lambda a: obj.finish()[4]
elif algorithm == 'ONMF':
method = obj.project
post = lambda a: np.concatenate(a, axis=1).T
_map = map(lambda thing: method(thing),
self._block_iterator(
flat_signal=True,
get=get,
signal_mask=signal_mask,
navigation_mask=navigation_mask))
H = []
try:
for thing in progressbar(
_map, total=nblocks, desc='Project'):
H.append(thing)
except KeyboardInterrupt:
pass
loadings = post(H)
if explained_variance is not None and \
explained_variance_ratio is None:
explained_variance_ratio = \
explained_variance / explained_variance.sum()
# RESHUFFLE "blocked" LOADINGS
ndim = self.axes_manager.navigation_dimension
try:
loadings = _reshuffle_mixed_blocks(
loadings,
ndim,
(output_dimension,),
nav_chunks).reshape((-1, output_dimension))
except ValueError:
# In case the projection step was not finished, it's left
# as scrambled
pass
finally:
self.data = original_data
target = self.learning_results
target.decomposition_algorithm = algorithm
target.output_dimension = output_dimension
target._object = obj
target.factors = factors
target.loadings = loadings
target.explained_variance = explained_variance
target.explained_variance_ratio = explained_variance_ratio示例2: decomposition
# 需要导入模块: from sklearn.decomposition import IncrementalPCA [as 别名]
# 或者: from sklearn.decomposition.IncrementalPCA import finish [as 别名]
#.........这里部分代码省略.........
if self._unfolded4decomposition is True:
self.fold()
self._unfolded4decomposition is False
else:
this_data = []
try:
for chunk in progressbar(
self._block_iterator(
flat_signal=True,
get=get,
signal_mask=signal_mask,
navigation_mask=navigation_mask),
total=nblocks,
leave=True,
desc='Learn'):
this_data.append(chunk)
if len(this_data) == num_chunks:
thedata = np.concatenate(this_data, axis=0)
method(thedata)
this_data = []
if len(this_data):
thedata = np.concatenate(this_data, axis=0)
method(thedata)
except KeyboardInterrupt:
pass
# GET ALREADY CALCULATED RESULTS
if algorithm == 'PCA':
explained_variance = obj.explained_variance_
explained_variance_ratio = obj.explained_variance_ratio_
factors = obj.components_.T
elif algorithm == 'ORPCA':
_, _, U, S, V = obj.finish()
factors = U * S
loadings = V
explained_variance = S**2 / len(factors)
elif algorithm == 'ONMF':
factors, loadings = obj.finish()
loadings = loadings.T
# REPROJECT
if reproject:
if algorithm == 'PCA':
method = obj.transform
def post(a): return np.concatenate(a, axis=0)
elif algorithm == 'ORPCA':
method = obj.project
obj.R = []
def post(a): return obj.finish()[4]
elif algorithm == 'ONMF':
method = obj.project
def post(a): return np.concatenate(a, axis=1).T
_map = map(lambda thing: method(thing),
self._block_iterator(
flat_signal=True,
get=get,
signal_mask=signal_mask,
navigation_mask=navigation_mask))
H = []
try: