scikit learn k means
from sklearn.cluster import KMeans
df = np.array([[1,4],[2,2],[2,5],[3,3],[3,4],[4,7],[5,6],[6,4],[6,7],[7,6],[7,9],[8,7],[8,9],[9,4],[9,8]])
kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10)
y_pred = kmeans.fit_predict(df)k-means clustering python
from sklearn.cluster import KMeans
kmeans = KMeans(init="random", n_clusters=3, n_init=10, max_iter=300, random_state=42 )
kmeans.fit(x_train) #Replace your training dataset instead of x_train
# The lowest SSE value
print(kmeans.inertia_)
# Final locations of the centroid
print(kmeans.cluster_centers_)
# The number of iterations required to converge
print(kmeans.n_iter_)
# first five predicted labels
print(kmeans.labels_[:5])
# init controls the initialization technique. The standard version of the k-means algorithm is implemented by setting init to "random". Setting this to "k-means++" employs an advanced trick to speed up convergence, which you’ll use later.
# n_clusters sets k for the clustering step. This is the most important parameter for k-means.
# n_init sets the number of initializations to perform. This is important because two runs can converge on different cluster assignments. The default behavior for the scikit-learn algorithm is to perform ten k-means runs and return the results of the one with the lowest SSE.
# max_iter sets the number of maximum iterations for each initialization of the k-means algorithm.kmeans python
# Function: K Means
# -------------
# K-Means is an algorithm that takes in a dataset and a constant
# k and returns k centroids (which define clusters of data in the
# dataset which are similar to one another).
def kmeans(dataSet, k):
# Initialize centroids randomly
numFeatures = dataSet.getNumFeatures()
centroids = getRandomCentroids(numFeatures, k)
# Initialize book keeping vars.
iterations = 0
oldCentroids = None
# Run the main k-means algorithm
while not shouldStop(oldCentroids, centroids, iterations):
# Save old centroids for convergence test. Book keeping.
oldCentroids = centroids
iterations += 1
# Assign labels to each datapoint based on centroids
labels = getLabels(dataSet, centroids)
# Assign centroids based on datapoint labels
centroids = getCentroids(dataSet, labels, k)
# We can get the labels too by calling getLabels(dataSet, centroids)
return centroidsCopyright © 2021 Codeinu
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