sklearn-som v. 1.1.0¶
Master Documentation¶
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class
sklearn_som.som.SOM(m=3, n=3, dim=3, lr=1, sigma=1, max_iter=3000, **kwargs)¶ The 2-D, rectangular grid self-organizing map class using Numpy.
Parameters
- m : int, default=3
The shape along dimension 0 (vertical) of the SOM.
- n : int, default=3
The shape along dimesnion 1 (horizontal) of the SOM.
- dim : int, default=3
The dimensionality (number of features) of the input space.
- lr : float, default=1
The initial step size for updating the SOM weights.
- sigma : float, optional
Optional parameter for magnitude of change to each weight. Does not update over training (as does learning rate). Higher values mean more aggressive updates to weights.
- max_iter : int, optional
Optional parameter to stop training if you reach this many interation.
- random_state : int, optional
Optional integer seed to the random number generator for weight initialization. This will be used to create a new instance of Numpy’s default random number generator (it will not call np.random.seed()). Specify an integer for deterministic results.
Methods
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fit(X, epochs=1, shuffle=True)¶ Fit the self organizing-map to the given data.
Parameters
- X : ndarray
Training data. Must have shape (n, self.dim) where n is the number of training samples.
- epochs : int, default=1
The number of times to loop through the training data when fitting.
- shuffle : bool, default True
Whether or not to randomize the order of train data when fitting. Can be seeded with np.random.seed() prior to calling fit.
Returns
- None
Fits the SOM to the given data but does not return anything.
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predict(X)¶ Predict cluster for each element in X.
Parameters
- X : ndarray
An ndarray of shape (n, self.dim) where n is the number of samples. The data to predict clusters for.
Returns
- labels : ndarray
An ndarray of shape (n,). The predicted cluster index for each item in X.
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transform(X)¶ Transform the data X into cluster distance space.
Parameters
- X : ndarray
Data of shape (n, self.dim) where n is the number of samples. The data to transform.
Returns
- transformed : ndarray
Transformed data of shape (n, self.n*self.m). The Euclidean distance from each item in X to each cluster center.
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fit_predict(X, **kwargs)¶ Convenience method for calling fit(X) followed by predict(X).
Parameters
- X : ndarray
Data of shape (n, self.dim). The data to fit and then predict.
- **kwargs
Optional keyword arguments for the .fit() method.
Returns
- labels : ndarray
ndarray of shape (n,). The index of the predicted cluster for each item in X (after fitting the SOM to the data in X).
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fit_transform(X, **kwargs)¶ Convenience method for calling fit(X) followed by transform(X).
Unlike in sklearn, this is not implemented more efficiently (the efficiency is the same as calling fit(X) directly followed by transform(X)).
Parameters
- X : ndarray
Data of shape (n, self.dim) where n is the number of samples.
- **kwargs
Optional keyword arguments for the .fit() method.
Returns
- transformed : ndarray
ndarray of shape (n, self.m*self.n). The Euclidean distance from each item in X to each cluster center.