本文整理汇总了Python中sklearn.discriminant_analysis方法的典型用法代码示例。如果您正苦于以下问题：Python sklearn.discriminant_analysis方法的具体用法？Python sklearn.discriminant_analysis怎么用？Python sklearn.discriminant_analysis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的用法示例。
在下文中一共展示了sklearn.discriminant_analysis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: __init__
# 需要导入模块: import sklearn [as 别名]
# 或者: from sklearn import discriminant_analysis [as 别名]
def __init__(self, catalogueconstructor=None, selection=None, **params):
cc = catalogueconstructor
self.waveforms = cc.get_some_waveforms()
if selection is None:
#~ waveforms = self.waveforms
raise NotImplementedError
else:
peaks_index, = np.nonzero(selection)
waveforms = cc.get_some_waveforms(peaks_index=peaks_index)
labels = cc.all_peaks[peaks_index]['cluster_label']
flatten_waveforms = waveforms.reshape(waveforms.shape[0], -1)
self.lda = sklearn.discriminant_analysis.LinearDiscriminantAnalysis()
self.lda.fit(flatten_waveforms, labels)
#In GlobalPCA all feature represent all channels
self.channel_to_features = np.ones((cc.nb_channel, self.lda._max_components), dtype='bool') 示例2: init_classifier_impl
# 需要导入模块: import sklearn [as 别名]
# 或者: from sklearn import discriminant_analysis [as 别名]
def init_classifier_impl(field_code: str, init_script: str):
if init_script is not None:
init_script = init_script.strip()
if not init_script:
from sklearn import tree as sklearn_tree
return sklearn_tree.DecisionTreeClassifier()
from sklearn import tree as sklearn_tree
from sklearn import neural_network as sklearn_neural_network
from sklearn import neighbors as sklearn_neighbors
from sklearn import svm as sklearn_svm
from sklearn import gaussian_process as sklearn_gaussian_process
from sklearn.gaussian_process import kernels as sklearn_gaussian_process_kernels
from sklearn import ensemble as sklearn_ensemble
from sklearn import naive_bayes as sklearn_naive_bayes
from sklearn import discriminant_analysis as sklearn_discriminant_analysis
from sklearn import linear_model as sklearn_linear_model
eval_locals = {
'sklearn_linear_model': sklearn_linear_model,
'sklearn_tree': sklearn_tree,
'sklearn_neural_network': sklearn_neural_network,
'sklearn_neighbors': sklearn_neighbors,
'sklearn_svm': sklearn_svm,
'sklearn_gaussian_process': sklearn_gaussian_process,
'sklearn_gaussian_process_kernels': sklearn_gaussian_process_kernels,
'sklearn_ensemble': sklearn_ensemble,
'sklearn_naive_bayes': sklearn_naive_bayes,
'sklearn_discriminant_analysis': sklearn_discriminant_analysis
}
return eval_script('classifier init script of field {0}'.format(field_code), init_script, eval_locals)