nDifferentDataSet = 20
stress = np.empty((nDifferentDataSet, 4))
for j, metric in enumerate(metrics):
clusterCounts = np.empty((nDifferentDataSet, ))
dist = DistanceMetric.get_metric(metric)
print("MDS Metric: {}".format(metric))
for i in range(nDifferentDataSet):
data = generateOneClusterData(DEFAULT_NUMBER_OF_FEATURES,
DEFAULT_NUMBER_OF_RECORDS_PER_CLASS,
DEFAULT_FEATURE_MEAN_RANGE,
i,
distribution="normal")
precomputedMetricData = dist.pairwise(data)
mds = MDS(n_components=8, n_jobs=-1, dissimilarity="precomputed")
mdsData = mds.fit_transform(precomputedMetricData)
optimalK = OptimalK(parallel_backend='joblib', n_jobs=-1)
clusterCount = optimalK(mdsData,
n_refs=3,
cluster_array=np.arange(1, 10))
clusterCounts[i] = clusterCount
stress[i, j] = mds.stress_
meanClusterCount[j] = np.mean(clusterCounts)
import numpy as np
from matplotlib import pyplot as plt
from sklearn.neighbors import DistanceMetric
from sklearn.manifold import MDS
from DataGenerator import generateOneClusterData
from Settings import (DEFAULT_FEATURE_MEAN_RANGE, DEFAULT_NUMBER_OF_FEATURES,
DEFAULT_NUMBER_OF_RECORDS_PER_CLASS,
DEFAULT_RANDOM_NUMBER_SEED)
metrics = ("euclidean", "manhattan", "chebyshev", "minkowski")
data = generateOneClusterData(DEFAULT_NUMBER_OF_FEATURES,
DEFAULT_NUMBER_OF_RECORDS_PER_CLASS,
DEFAULT_FEATURE_MEAN_RANGE,
DEFAULT_RANDOM_NUMBER_SEED,
distribution="uniform")
for i, metric in enumerate(metrics):
dist = DistanceMetric.get_metric(metric)
precomputedMetricData = dist.pairwise(data)
mds = MDS(n_components=2, n_jobs=-1, dissimilarity="precomputed")
mdsData = mds.fit_transform(precomputedMetricData)
OPACITY = 0.7
plt.subplot(2,2,i+1)
plt.title("Data Set - {}".format(metric))
plt.xlabel("MDS Feature 1")
plt.ylabel("MDS Feature 2")
plt.scatter(mdsData[:,0], mdsData[:,1],
c=np.random.rand(1,3), alpha=OPACITY)