apollon.som.som module

class apollon.som.som.BatchMap(dims: Tuple[int, int, int], n_iter: int, eta: float, nhr: float, nh_shape: str = 'gaussian', init_weights: Union[Callable[[numpy.ndarray, Tuple[int, int]], numpy.ndarray], str] = 'rnd', metric: Union[Callable[[numpy.ndarray, numpy.ndarray], float], str] = 'euclidean', seed: Optional[int] = None)

Bases: apollon.som.som.SomBase

class apollon.som.som.IncrementalKDTReeMap(dims: tuple, n_iter: int, eta: float, nhr: float, nh_shape: str = 'star2', init_distr: str = 'uniform', metric: str = 'euclidean', seed: Optional[int] = None)

Bases: apollon.som.som.SomBase

fit(train_data, verbose=False)

Fit SOM to input data.

class apollon.som.som.IncrementalMap(dims: Tuple[int, int, int], n_iter: int, eta: float, nhr: float, nh_shape: str = 'gaussian', init_weights: Union[Callable[[numpy.ndarray, Tuple[int, int]], numpy.ndarray], str] = 'rnd', metric: Union[Callable[[numpy.ndarray, numpy.ndarray], float], str] = 'euclidean', seed: Optional[int] = None)

Bases: apollon.som.som.SomBase

fit(train_data, verbose=False, output_weights=False)

class apollon.som.som.SomBase(dims: Tuple[int, int, int], n_iter: int, eta: float, nhr: float, nh_shape: str, init_weights: Union[Callable[[numpy.ndarray, Tuple[int, int]], numpy.ndarray], str], metric: Union[Callable[[numpy.ndarray, numpy.ndarray], float], str], seed: Optional[float] = None)

Bases: object

calibrate(data: numpy.ndarray, target: numpy.ndarray) → numpy.ndarray

Retrieve the target value of the best matching input data vector for each unit weight vector.

Parameters

• data – Input data set.

• target – Target labels.

Returns

Array of target values.

property dims

Return the SOM dimensions.

distribute(data: numpy.ndarray) → Dict[int, List[int]]

Distribute the vectors of data on the SOM.

Indices of vectors n data are mapped to the index of their best matching unit.

Parameters

data – Input data set.

Returns

Dictionary with SOM unit indices as keys. Each key maps to a list that holds the indices of rows in data, which best match this key.

property dists

Return the distance matrix of the grid points.

property dw

Return the dimension of the weight vectors.

property dx

Return the number of units along the first dimension.

property dy

Return the number of units along the second dimension.

property grid

Return the grid.

property hit_counts

Return total hit counts for each SOM unit.

match(data: numpy.ndarray) → numpy.ndarray

Return the multi index of the best matching unit for each vector in data.

Caution: This function returns the multi index into the array.

Parameters

data – Input data set.

Returns

Array of SOM unit indices.

match_flat(data: numpy.ndarray) → numpy.ndarray

Return the index of the best matching unit for each vector in data.

Parameters

data – Input data set.

Returns

Array of SOM unit indices.

property n_units

Return the total number of units on the SOM.

predict(data: numpy.ndarray) → numpy.ndarray

Predict the SOM index of the best matching unit for each item in data.

Parameters

data – Input data. Rows are items, columns are features.

Returns

One-dimensional array of indices.

property quantization_error

Return quantization error.

save(path) → None

Save som object to file using pickle.

Parameters

path – Save SOM to this path.

save_weights(path) → None

Save weights only.

Parameters

path – File path

property shape

Return the map shape.

property topographic_error

Return topographic error.

transform(data: numpy.ndarray) → numpy.ndarray

Transform each item in data to feature space.

This, in principle, returns best matching unit’s weight vectors.

Parameters

data – Input data. Rows are items, columns are features.

Returns

Position of each data item in the feature space.

umatrix(radius: int = 1, scale: bool = True, norm: bool = True)

Compute U-matrix of SOM instance.

Parameters

• radius – Map neighbourhood radius.

• scale – If True, scale each U-height by the number of the associated unit’s neighbours.

• norm – Normalize U-matrix if True.

Returns

Unified distance matrix.

property weights

Return the weight vectors.

class apollon.som.som.SomGrid(shape: Tuple[int, int])

Bases: object

cr()

nhb(point: Tuple[int, int], radius: float) → numpy.ndarray

Compute neighbourhood within radius around pouint.

Parameters

• point – Coordinate in a two-dimensional array.

• radius – Lenght of radius.

Returns

Array of positions of neighbours.

nhb_idx(point: Tuple[int, int], radius: float) → numpy.ndarray

Compute the neighbourhood within radius around point.

Parameters

• point – Coordinate in a two-dimensional array.

• radius – Lenght of radius.

Returns

Array of indices of neighbours.

rc()