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Added Clustering.py

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moved kmeans from Analysis to Clustering
This commit is contained in:
Arthur Lu 2021-05-26 07:41:32 +00:00
parent 8215f95706
commit 8ff2c2f0c0
2 changed files with 32 additions and 2 deletions
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4
analysis-master/tra_analysis/Analysis.py
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@ -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)

30
analysis-master/tra_analysis/Clustering.py Normal file
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@ -0,0 +1,30 @@
# 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__ = "1.0.0"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.0.0:
- created this submodule
- copied kmeans clustering from Analysis
"""
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
)
__all__ = [
]
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)
kernel.fit(data)
predictions = kernel.predict(data)
centers = kernel.cluster_centers_
return centers, predictions