10000 MCD Exact Fit: Fixes Issue #3367 by ThatGeoGuy · Pull Request #4635 · scikit-learn/scikit-learn · GitHub
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MCD Exact Fit: Fixes Issue #3367 #4635

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MCD Exact Fit: Fixes Issue #3367 #4635

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b17d375
Fixes issue where 1d arrays improperly reshape
ThatGeoGuy Apr 3, 2015
71d016f
Changes warning message if 1Darray passed to fast_mcd
ThatGeoGuy Apr 3, 2015
8ec134f
temp change to singular covariance
ThatGeoGuy Apr 5, 2015
b124b1c
Adds break condition to determinant check in _c_step
ThatGeoGuy Apr 5, 2015
2226a65
Fixes issue with singular matrices
ThatGeoGuy Apr 6, 2015
ea9b2e5
Fixes convergence criteria for exact fit scenario
ThatGeoGuy Apr 26, 2015
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19 changes: 6 additions & 13 deletions sklearn/covariance/robust_covariance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ def _c_step(X, n_support, random_state, remaining_iterations=30,
# Iterative procedure for Minimum Covariance Determinant computation
det = fast_logdet(covariance)
previous_det = np.inf
while (det < previous_det) and (remaining_iterations > 0):
while (det < previous_det) and (remaining_iterations > 0) and not (np.isinf(det)):
# save old estimates values
previous_location = location
previous_covariance = covariance
Expand All @@ -140,16 +140,8 @@ def _c_step(X, n_support, random_state, remaining_iterations=30,

previous_dist = dist
dist = (np.dot(X - location, precision) * (X - location)).sum(axis=1)
# Catch computation errors
if np.isinf(det):
raise ValueError(
"Singular covariance matrix. "
"Please check that the covariance matrix corresponding "
"to the dataset is full rank and that MinCovDet is used with "
"Gaussian-distributed data (or at least data drawn from a "
"unimodal, symmetric distribution.")
# Check convergence
if np.allclose(det, previous_det):
if np.isinf(det) or np.allclose(det, previous_det):
# c_step procedure converged
if verbose:
print("Optimal couple (location, covariance) found before"
Expand Down Expand Up @@ -361,9 +353,10 @@ def fast_mcd(X, support_fraction=None,

X = np.asarray(X)
if X.ndim == 1:
X = np.reshape(X, (1, -1))
warnings.warn("Only one sample available. "
"You may want to reshape your data array")
X = np.reshape(X, (-1, 1))
warnings.warn("1D array passed in. "
"Assuming the array contains samples, not features. "
"You may wish to reshape your data.")
n_samples, n_features = X.shape

# minimum breakdown value
Expand Down
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