diff --git a/analysis-master/test_analysis.py b/analysis-master/test_analysis.py index ebba8da5..1668c719 100644 --- a/analysis-master/test_analysis.py +++ b/analysis-master/test_analysis.py @@ -5,6 +5,7 @@ from sklearn import metrics from tra_analysis import Analysis as an from tra_analysis import Array from tra_analysis import ClassificationMetric +from tra_analysis import Clustering from tra_analysis import CorrelationTest from tra_analysis import Fit from tra_analysis import KNN @@ -230,4 +231,18 @@ def test_equation(): "-(sgn(cos(PI/4)))": -1, } for key in list(correctParse.keys()): - assert parser.eval(key) == correctParse[key] \ No newline at end of file + assert parser.eval(key) == correctParse[key] + +def test_clustering(): + + normalizer = sklearn.preprocessing.Normalizer() + + data = X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]]) + + assert Clustering.dbscan(data, eps=3, min_samples=2).tolist() == [0, 0, 0, 1, 1, -1] + assert Clustering.dbscan(data, normalizer=normalizer, eps=3, min_samples=2).tolist() == [0, 0, 0, 0, 0, 0] + + data = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]]) + + assert Clustering.spectral(data, n_clusters=2, assign_labels='discretize', random_state=0).tolist() == [1, 1, 1, 0, 0, 0] + assert Clustering.spectral(data, normalizer=normalizer, n_clusters=2, assign_labels='discretize', random_state=0).tolist() == [0, 1, 1, 0, 0, 0] \ No newline at end of file diff --git a/analysis-master/tra_analysis/Analysis.py b/analysis-master/tra_analysis/Analysis.py index 6a8a63b3..df81951c 100644 --- a/analysis-master/tra_analysis/Analysis.py +++ b/analysis-master/tra_analysis/Analysis.py @@ -599,7 +599,7 @@ def npmin(data): def npmax(data): return np.amax(data) - +""" need to decide what to do with this function def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm) @@ -608,7 +608,7 @@ def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0. centers = kernel.cluster_centers_ return centers, predictions - +""" def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None): kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state) diff --git a/analysis-master/tra_analysis/Clustering.py b/analysis-master/tra_analysis/Clustering.py new file mode 100644 index 00000000..8b7cef6a --- /dev/null +++ b/analysis-master/tra_analysis/Clustering.py @@ -0,0 +1,61 @@ +# Titan Robotics Team 2022: Clustering submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import Clustering' +# setup: + +__version__ = "2.0.1" + +# changelog should be viewed using print(analysis.__changelog__) +__changelog__ = """changelog: + 2.0.1: + - added normalization preprocessing to clustering, expects instance of sklearn.preprocessing.Normalizer() + 2.0.0: + - added dbscan clustering algo + - added spectral clustering algo + 1.0.0: + - created this submodule + - copied kmeans clustering from Analysis +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ + "kmeans", + "dbscan", + "spectral", +] + +import sklearn + +def kmeans(data, normalizer = None, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): + + if normalizer != None: + data = normalizer.transform(data) + + kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm) + kernel.fit(data) + predictions = kernel.predict(data) + centers = kernel.cluster_centers_ + + return centers, predictions + +def dbscan(data, normalizer=None, eps=0.5, min_samples=5, metric='euclidean', metric_params=None, algorithm='auto', leaf_size=30, p=None, n_jobs=None): + + if normalizer != None: + data = normalizer.transform(data) + + model = sklearn.cluster.DBSCAN(eps = eps, min_samples = min_samples, metric = metric, metric_params = metric_params, algorithm = algorithm, leaf_size = leaf_size, p = p, n_jobs = n_jobs).fit(data) + + return model.labels_ + +def spectral(data, normalizer=None, n_clusters=8, eigen_solver=None, n_components=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, verbose=False): + + if normalizer != None: + data = normalizer.transform(data) + + model = sklearn.cluster.SpectralClustering(n_clusters = n_clusters, eigen_solver = eigen_solver, n_components = n_components, random_state = random_state, n_init = n_init, gamma = gamma, affinity = affinity, n_neighbors = n_neighbors, eigen_tol = eigen_tol, assign_labels = assign_labels, degree = degree, coef0 = coef0, kernel_params = kernel_params, n_jobs = n_jobs).fit(data) + + return model.labels_ \ No newline at end of file diff --git a/analysis-master/tra_analysis/__init__.py b/analysis-master/tra_analysis/__init__.py index 9f249496..0f170bdd 100644 --- a/analysis-master/tra_analysis/__init__.py +++ b/analysis-master/tra_analysis/__init__.py @@ -60,6 +60,7 @@ __all__ = [ from . import Analysis as Analysis from .Array import Array from .ClassificationMetric import ClassificationMetric +from . import Clustering from . import CorrelationTest from .equation import Expression from . import Fit