NormalizedDotProductMetric#
- class kikuchipy.indexing.NormalizedDotProductMetric(n_experimental_patterns: int | None = None, n_dictionary_patterns: int | None = None, navigation_mask: ndarray | None = None, signal_mask: ndarray | None = None, dtype: str | dtype | type = 'float32', rechunk: bool = False)[source]#
Bases:
SimilarityMetric
Similarity metric implementing the normalized dot product [Chen et al., 2015].
The metric is defined as
\[\rho = \frac {\langle \mathbf{X}, \mathbf{Y} \rangle} {||\mathbf{X}|| \cdot ||\mathbf{Y}||},\]where \({\langle \mathbf{X}, \mathbf{Y} \rangle}\) is the dot (inner) product of the pattern vectors \(\mathbf{X}\) and \(\mathbf{Y}\).
See
SimilarityMetric
for the description of the initialization parameters and the list of attributes.Attributes
Methods
Match all experimental patterns to all dictionary patterns and return their similarities.
Prepare dictionary patterns before matching to experimental patterns in
match()
.Prepare experimental patterns before matching to dictionary patterns in
match()
.