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analysis.py v 1.1.9.002

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art 2019-11-08 12:26:42 -06:00
parent 93f87dbdc1
commit fa6e42e2ee
1 changed files with 7 additions and 4 deletions
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data analysis/analysis/analysis.py
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@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.9.001"
__version__ = "1.1.9.002"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.9.002:
- kernelized PCA and KNN
1.1.9.001:
- fixed bugs with SVM and NaiveBayes
1.1.9.000:
@ -397,17 +399,18 @@ def variance(data):
return np.var(data)
def kmeans(data, kernel=sklearn.cluster.KMeans()):
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
def pca(data, n_components = None, copy = True, whiten = False, svd_solver = ’auto’, tol = 0.0, iterated_power = ’auto’, random_state = None):
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()
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)
return kernel.fit_transform(data)