本文整理汇总了Python中sklearn.cluster.WardAgglomeration.inverse_transform方法的典型用法代码示例。如果您正苦于以下问题：Python WardAgglomeration.inverse_transform方法的具体用法？Python WardAgglomeration.inverse_transform怎么用？Python WardAgglomeration.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.cluster.WardAgglomeration的用法示例。
在下文中一共展示了WardAgglomeration.inverse_transform方法的3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: test_ward_agglomeration
# 需要导入模块: from sklearn.cluster import WardAgglomeration [as 别名]
# 或者: from sklearn.cluster.WardAgglomeration import inverse_transform [as 别名]
def test_ward_agglomeration():
"""
Check that we obtain the correct solution in a simplistic case
"""
rnd = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
X = rnd.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
ward = WardAgglomeration(n_clusters=5, connectivity=connectivity)
ward.fit(X)
assert_true(np.size(np.unique(ward.labels_)) == 5)
Xred = ward.transform(X)
assert_true(Xred.shape[1] == 5)
Xfull = ward.inverse_transform(Xred)
assert_true(np.unique(Xfull[0]).size == 5)示例2: representation
# 需要导入模块: from sklearn.cluster import WardAgglomeration [as 别名]
# 或者: from sklearn.cluster.WardAgglomeration import inverse_transform [as 别名]
first_epi = nifti_masker.inverse_transform(fmri_masked[0]).get_data()
first_epi = np.ma.masked_array(first_epi, first_epi == 0)
# Outside the mask: a uniform value, smaller than inside the mask
first_epi[np.logical_not(mask)] = 0.9 * first_epi[mask].min()
vmax = first_epi[..., 20].max()
vmin = first_epi[..., 20].min()
pl.imshow(np.rot90(first_epi[..., 20]),
interpolation='nearest', cmap=pl.cm.spectral, vmin=vmin, vmax=vmax)
pl.axis('off')
pl.title('Original (%i voxels)' % fmri_masked.shape[1])
# A reduced data can be create by taking the parcel-level average:
# Note that, as many objects in the scikit-learn, the ward object exposes
# a transform method that modifies input features. Here it reduces their
# dimension
fmri_reduced = ward.transform(fmri_masked)
# Display the corresponding data compressed using the parcellation
fmri_compressed = ward.inverse_transform(fmri_reduced)
compressed = nifti_masker.inverse_transform(
fmri_compressed[0]).get_data()
compressed = np.ma.masked_equal(compressed, 0)
pl.figure()
pl.imshow(np.rot90(compressed[:, :, 20]),
interpolation='nearest', cmap=pl.cm.spectral, vmin=vmin, vmax=vmax)
pl.title('Compressed representation (2000 parcels)')
pl.axis('off')
pl.show()
示例3:
# 需要导入模块: from sklearn.cluster import WardAgglomeration [as 别名]
# 或者: from sklearn.cluster.WardAgglomeration import inverse_transform [as 别名]
labels[mask] = ward.labels_
cut = labels[:, :, 20].astype(np.int)
colors = np.random.random(size=(ward.n_clusters + 1, 3))
colors[-1] = 0
pl.axis('off')
pl.imshow(colors[cut], interpolation='nearest')
pl.title('Ward parcellation')
# Display the original data
pl.figure()
first_epi_img = epi_img[..., 0].copy()
first_epi_img[np.logical_not(mask)] = 0
pl.imshow(first_epi_img[..., 20], interpolation='nearest',
cmap=pl.cm.spectral)
pl.axis('off')
pl.title('Original')
# Display the corresponding data compressed using the parcellation
X_r = ward.transform(epi_masked.T)
X_c = ward.inverse_transform(X_r)
compressed_img = np.zeros(mask.shape)
compressed_img[mask] = X_c[0]
pl.figure()
pl.imshow(compressed_img[:, :, 20], interpolation='nearest',
cmap=pl.cm.spectral)
pl.title('Compressed representation')
pl.axis('off')
pl.show()