diff --git a/src/apollon/som/som.py b/src/apollon/som/som.py
index e4cc4121d9840be8a185a4e6fd4fb3f1582d457c..e0a8f0b99fbc5c1d7857fc86305a75d20606055a 100644
--- a/src/apollon/som/som.py
+++ b/src/apollon/som/som.py
@@ -11,11 +11,10 @@ from apollon.io import io as aio
from apollon.som import defaults as _defaults
from . import neighbors as _neighbors
from . import utilities as asu
-from .. types import Array, Shape, Coord
+from .. types import Array, Shape, SomDims, Coord
WeightInit = Union[Callable[[Array, Shape], Array], str]
Metric = Union[Callable[[Array, Array], float], str]
-SomDims = Tuple[int, int, int]
class SomGrid:
@@ -320,7 +319,7 @@ class IncrementalMap(SomBase):
seed=seed)
def fit(self, train_data, verbose=False, output_weights=False):
- self._weights = self.init_weights(train_data, self.shape)
+ self._weights = self.init_weights(self.dims, train_data)
eta_ = asu.decrease_linear(self.init_eta, self.n_iter, _defaults.final_eta)
nhr_ = asu.decrease_expo(self.init_nhr, self.n_iter, _defaults.final_nhr)
diff --git a/src/apollon/som/utilities.py b/src/apollon/som/utilities.py
index 64ef5a7885c9d29bd79df52f01ad451122d94237..2e2b577037a77d618f093b87a6a26fba1cc59f5a 100644
--- a/src/apollon/som/utilities.py
+++ b/src/apollon/som/utilities.py
@@ -7,13 +7,13 @@ Copyright (C) 2019 Michael Blaß
mblass@posteo.net
"""
import itertools
-from typing import Dict, Iterable, Iterator, List, Tuple
+from typing import Dict, Iterable, Iterator, List, Optional, Tuple
import numpy as np
from scipy.spatial import distance as _distance
from scipy import stats as _stats
-from apollon.types import Array, Shape
+from apollon.types import Array, Shape, SomDims
from apollon import tools
@@ -115,13 +115,13 @@ def best_match(weights: Array, inp: Array, metric: str):
return dists.argmin(axis=0), dists.min(axis=0)
-def sample_pca(data: Array, shape: Shape, adapt: bool = True) -> Array:
+def sample_pca(dims: SomDims, data: Optional[Array] = None, **kwargs) -> Array:
"""Compute initial SOM weights by sampling from the first two principal
components of the input data.
Args:
+ dims: Dimensions of SOM.
data: Input data set.
- shape: Shape of SOM.
adapt: If ``True``, the largest value of ``shape`` is applied to the
principal component with the largest sigular value. This
orients the map, such that map dimension with the most units
@@ -130,36 +130,45 @@ def sample_pca(data: Array, shape: Shape, adapt: bool = True) -> Array:
Returns:
Array of SOM weights.
"""
+ n_rows, n_cols, n_feats = dims
+ n_units = n_rows * n_cols
+ if data is None:
+ data = np.random.randint(-100, 100, (300, n_feats))
vals, vects, trans_data = tools.pca(data, 2)
data_limits = np.column_stack((trans_data.min(axis=0),
trans_data.max(axis=0)))
- if adapt:
+ if 'adapt' in kwargs and kwargs['adapt'] is True:
shape = sorted(shape, reverse=True)
- dim_x = np.linspace(*data_limits[0], shape[0])
- dim_y = np.linspace(*data_limits[1], shape[1])
+ dim_x = np.linspace(*data_limits[0], n_rows)
+ dim_y = np.linspace(*data_limits[1], n_cols)
grid_x, grid_y = np.meshgrid(dim_x, dim_y)
points = np.vstack((grid_x.ravel(), grid_y.ravel()))
weights = points.T @ vects + data.mean(axis=0)
return weights
-def sample_rnd(data: Array, shape: Shape) -> Array:
+def sample_rnd(dims: SomDims, data: Optional[Array] = None, **kwargs) -> Array:
"""Compute initial SOM weights by sampling uniformly from the data space.
Args:
- data: Input data set
- shape: Shape of SOM.
+ dims: Dimensions of SOM.
+ data: Input data set. If ``None``, sample from [-10, 10].
Returns:
Array of SOM weights.
"""
- n_units = np.prod(shape)
- data_limits = np.column_stack((data.max(axis=0), data.min(axis=0)))
+ n_rows, n_cols, n_feats = dims
+ n_units = n_rows * n_cols
+ if data is not None:
+ data_limits = np.column_stack((data.min(axis=0), data.max(axis=0)))
+ else:
+ data_limits = np.random.randint(-10, 10, (n_feats, 2))
+ data_limits.sort()
weights = [np.random.uniform(*lim, n_units) for lim in data_limits]
return np.column_stack(weights)
-def sample_stm(data: Array, shape: Shape):
+def sample_stm(dims: SomDims, data: Optional[Array] = None, **kwargs) -> Array:
"""Compute initial SOM weights by sampling stochastic matrices from
Dirichlet distribution.
@@ -170,8 +179,8 @@ def sample_stm(data: Array, shape: Shape):
The square root of the weight vectors' size must be a real integer.
Args:
- data: Input data set.
- shape: Shape of SOM.
+ dims: Dimensions of SOM.
+ data: Input data set.
Returns:
Array of SOM weights.
@@ -182,20 +191,35 @@ def sample_stm(data: Array, shape: Shape):
are a discrete probability distribution forming the ``N``th row of
the matrix.
"""
- n_rows = np.sqrt(data.shape[1])
- if bool(n_rows - int(n_rows)):
- msg = (f'Weight vector with {n_rows} elements is not '
+ n_rows, n_cols, n_feats = dims
+ n_states = np.sqrt(n_feats)
+ if bool(n_states - int(n_states)):
+ msg = (f'Weight vector with {n_feats} elements is not '
'reshapeable to square matrix.')
raise ValueError(msg)
- n_rows = int(n_rows)
- n_units = np.prod(shape)
- alpha = np.random.randint(1, 10, (n_rows, n_rows))
- st_matrix = np.hstack([_stats.dirichlet.rvs(alpha=a, size=n_units)
+ n_states = int(n_states)
+ n_units = n_rows * n_cols
+ alpha = np.random.randint(1, 10, (n_states, n_states))
+ st_matrix = np.hstack([_stats.dirichlet(a).rvs(size=n_units)
for a in alpha])
return st_matrix
+def sample_hist(dims: SomDims, data: Optional[Array] = None, **kwargs) -> Array:
+ """Sample sum-normalized histograms.
+
+ Args:
+ dims: Dimensions of SOM.
+ data: Input data set.
+
+ Returns:
+ Two-dimensional array in which each row is a historgram.
+ """
+ n_rows, n_cols, n_feats = dims
+ return _stats.dirichlet(np.ones(n_feats)).rvs(n_rows*n_cols)
+
+
def distribute(bmu_idx: Iterable[int], n_units: int
) -> Dict[int, List[int]]:
"""List training data matches per SOM unit.
@@ -223,4 +247,5 @@ def distribute(bmu_idx: Iterable[int], n_units: int
weight_initializer = {
'rnd': sample_rnd,
'stm': sample_stm,
- 'pca': sample_pca,}
+ 'pca': sample_pca,
+ 'hist': sample_hist}
diff --git a/src/apollon/types.py b/src/apollon/types.py
index 06d0827eb4ed03d6a30788aeca41751690b288e6..0b0619ad6c4319888f749d1d2317d8f21dead7ca 100644
--- a/src/apollon/types.py
+++ b/src/apollon/types.py
@@ -20,6 +20,7 @@ PathGen = Generator[PathType, None, None]
Schema = Dict[str, Collection[str]]
Shape = Tuple[int, int]
+SomDims = Tuple[int, int, int]
Coord = Tuple[int, int]
AdIndex = Tuple[List[int], List[int]]
diff --git a/tests/som/test_som_utilities.py b/tests/som/test_utilities.py
similarity index 72%
rename from tests/som/test_som_utilities.py
rename to tests/som/test_utilities.py
index 9b46bcaaebc9515fc338361adfbcde8a63921b81..a8c61e5243c6c942064264bca35ca2bc54368fbd 100644
--- a/tests/som/test_som_utilities.py
+++ b/tests/som/test_utilities.py
@@ -1,12 +1,15 @@
import unittest
from hypothesis import strategies as hst
+from hypothesis import given
import numpy as np
from scipy.spatial import distance
-import scipy as sp
from apollon.som import utilities as asu
+from apollon.types import SomDims
+dimension = hst.integers(min_value=2, max_value=50)
+som_dims = hst.tuples(dimension, dimension, dimension)
"""
class TestMatch(unittest.TestCase):
def setUp(self) -> None:
@@ -39,6 +42,24 @@ class TestDistribute(unittest.TestCase):
self.assertIsInstance(res, dict)
+class TestSampleHist(unittest.TestCase):
+ def setUp(self) -> None:
+ pass
+
+ @given(som_dims)
+ def test_rows_are_stochastic(self, dims: SomDims) -> None:
+ weights = asu.sample_hist(dims)
+ comp =np.isclose(weights.sum(axis=1), 1)
+ self.assertTrue(comp.all())
+
+
+class TestSamplePca(unittest.TestCase):
+ def setUp(self) -> None:
+ pass
+
+ @given(som_dims)
+ def test_x(self, dims: SomDims) -> None:
+ weights = asu.sample_pca(dims)
"""
class TestSelfOrganizingMap(unittest.TestCase):
def setUp(self):