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diff --git a/src/utils/algorithms/__init__.py b/src/utils/algorithms/__init__.py
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diff --git a/src/utils/algorithms/callbacks.py b/src/utils/algorithms/callbacks.py
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diff --git a/src/utils/algorithms/loader.py b/src/utils/algorithms/loader.py
deleted file mode 100644
index eb34d3ae213946b18a21eef4d175ae5ff15d5663..0000000000000000000000000000000000000000
--- a/src/utils/algorithms/loader.py
+++ /dev/null
@@ -1,49 +0,0 @@
-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)
diff --git a/src/utils/data_processing/__init__.py b/src/utils/data_processing/__init__.py
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diff --git a/src/utils/data_processing/callbacks.py b/src/utils/data_processing/callbacks.py
deleted file mode 100644
index 6df69d20b5c883b9934ff8a6673cd5455ecf2ebd..0000000000000000000000000000000000000000
--- a/src/utils/data_processing/callbacks.py
+++ /dev/null
@@ -1,51 +0,0 @@
-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])
\ No newline at end of file
diff --git a/src/utils/data_processing/synthetic.py b/src/utils/data_processing/synthetic.py
deleted file mode 100644
index 9cfb80634596929639cca352d3e15559b1b2f82d..0000000000000000000000000000000000000000
--- a/src/utils/data_processing/synthetic.py
+++ /dev/null
@@ -1,49 +0,0 @@
-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
-
-
\ No newline at end of file