remove deleted files

src/utils/algorithms/loader.py

-from sklearn import tree
-from sklearn.preprocessing import StandardScaler
-
-
-def load_algorithm(algorithm):
-    # normalize dataset for easier parameter selection
-    
-    # estimate bandwidth for mean shift
-    # bandwidth = cluster.estimate_bandwidth(X, quantile=0.3)
-
-    # connectivity matrix for structured Ward
-    # connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)
-
-    # make connectivity symmetric
-    # connectivity = 0.5 * (connectivity + connectivity.T)
-
-    # # Generate the new colors:
-    if algorithm=='MiniBatchKMeans':
-        model = tree.DecisionTreeClassifier()
-
-    # elif algorithm=='Birch':
-    #     model = cluster.Birch(n_clusters=n_clusters)
-
-    # elif algorithm=='DBSCAN':
-    #     model = cluster.DBSCAN(eps=.2)
-
-    # elif algorithm=='AffinityPropagation':
-    #     model = cluster.AffinityPropagation(damping=.9,
-    #                                         preference=-200)
-
-    # elif algorithm=='MeanShift':
-    #     model = cluster.MeanShift(bandwidth=bandwidth,
-    #                               bin_seeding=True)
-
-    # elif algorithm=='SpectralClustering':
-    #     model = cluster.SpectralClustering(n_clusters=n_clusters,
-    #                                        eigen_solver='arpack',
-    #                                        affinity="nearest_neighbors")
-
-    # elif algorithm=='Ward':
-    #     model = cluster.AgglomerativeClustering(n_clusters=n_clusters,
-    #                                             linkage='ward',
-    #                                             connectivity=connectivity)
-
-    # elif algorithm=='AgglomerativeClustering':
-    #     model = cluster.AgglomerativeClustering(linkage="average",
-    #                                             affinity="cityblock",
-    #                                             n_clusters=n_clusters,
-    #                                             connectivity=connectivity)

src/utils/data_processing/callbacks.py

-import numpy as np
-import math
-
-from utils.data_processing.synthetic import synthetic_dataset
-
-from bokeh.io import curdoc, show, output_notebook
-from bokeh.layouts import column, row
-from bokeh.models import ColumnDataSource, Select, Slider, Plot, Scatter
-from bokeh.palettes import Spectral6
-from bokeh.plotting import figure
-
-spectral = np.hstack([Spectral6] * 20)
-n_clusters_p_class = 1
-
-def update_samples_or_dataset(attrname, 
-                              old, 
-                              new, 
-                            #   dataset_select,
-                            #   samples_slider,
-                            #   classes_slider,
-                            #   features_slider,
-                            #   inf_slider,
-                            #   source
-                              ):
-    global x, y
-
-    dataset = dataset_select.value
-    n_samples = int(samples_slider.value)
-    n_classes = int(classes_slider.value)
-    n_features = int(features_slider.value)
-    n_inf = int(inf_slider.value)
-
-    if n_inf > n_features:
-        n_features = n_inf
-        features_slider.update(value=n_inf)
-    
-    if n_classes * n_clusters_p_class > 2**n_inf:
-        
-        # n_inf = math.floor(math.sqrt(n_classes*n_clusters_p_class)) + n_classes % 2
-
-        n_inf = (math.ceil(math.log2(n_classes)))
-        n_features = n_inf
-        # print("this is v", n_inf)
-        
-        inf_slider.update(value=n_inf)
-        features_slider.update(value=n_features)
-
-    x, y = synthetic_dataset(dataset, n_samples, n_inf, n_features, n_classes)
-    colors = [spectral[i] for i in y]
-
-    source.data = dict(colors=colors, x=x[:, 0], y=x[:, 1])
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src/utils/data_processing/synthetic.py

-import numpy as np
-from sklearn import datasets
-
-class SyntheticData:
-    def __init__(self, 
-                 dataset='Make Classification', 
-                 n_samples=1500, 
-                 n_features=4, 
-                 n_classes=3, 
-                 n_inf=2):
-        self.dataset = dataset
-        self.n_samples = n_samples
-        self.n_features = n_features
-        self.n_classes = n_classes
-        self.n_inf = n_inf
-    
-    def generator(self):
-        if self.dataset == 'Blobs':
-            return datasets.make_blobs(n_samples=self.n_samples,
-                                    random_state=8)
-            
-        elif self.dataset == 'Make Classification':
-            return datasets.make_classification(n_samples=self.n_samples,
-                                                n_features=self.n_features, 
-                                                n_informative=self.n_inf, 
-                                                n_redundant=0,
-                                                n_clusters_per_class=1, 
-                                                n_classes=self.n_classes,
-                                                random_state=8)
-            
-        # if dataset == 'Noisy Circles':
-        #     return datasets.make_circles(n_samples=n_samples,
-        #                                 factor=0.5,
-        #                                 noise=0.05)
-
-        # elif dataset == 'Noisy Moons':
-        #     return datasets.make_moons(n_samples=n_samples,
-        #                             noise=0.05)
-
-        # elif dataset == 'Multilabel Classification':
-        #     return datasets.make_multilabel_classification(n_samples=n_samples, 
-        #                                                 n_features=n_features,
-        #                                                 n_classes=n_classes,
-        #                                                 random_state=8)
-
-        elif self.dataset == "No Structure":
-            return np.random.rand(self.n_samples, 2), None
-        
-        
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