completed 2 clustering parts of unsupervised learning section

python/Unsupervised Learning/Clustering/helpers2.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.cluster import KMeans
+from sklearn.datasets import make_blobs
+
+def simulate_data(n = 500, features = 10, centroids = 3):
+    '''
+    Simulates n data points, each with number of features equal to features, with a number of centers equal to centroids
+    INPUT (defaults)
+        n = number of rows (500)
+        features = number of columns (10)
+        centroids = number of centers (3)
+    Output
+        dataset = a dataset with the the specified characteristics
+    '''
+    dataset, y = make_blobs(n_samples=n, n_features=features, centers=centroids, random_state=42)
+
+    return dataset
+
+def plot_data(data, labels):
+    '''
+    Plot data with colors associated with labels
+    '''
+    fig = plt.figure();
+    ax = Axes3D(fig)
+    ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=labels, cmap='tab10');
+
+data = simulate_data(200, 5, 4)
+
+def get_kmeans_score(data, center):
+    '''
+    returns the kmeans score regarding SSE for points to centers
+    INPUT:
+        data - the dataset you want to fit kmeans to
+        center - the number of centers you want (the k value)
+    OUTPUT:
+        score - the SSE score for the kmeans model fit to the data
+    '''
+    #instantiate kmeans
+    kmeans = KMeans(n_clusters=center)
+
+    # Then fit the model to your data using the fit method
+    model = kmeans.fit(data)
+
+    # Obtain a score related to the model fit
+    score = np.abs(model.score(data))
+
+    return score
+
+def fit_mods():
+    scores = []
+    centers = list(range(1,11))
+
+    for center in centers:
+        scores.append(get_kmeans_score(data, center))
+
+    return centers, scores