<jupyter_start><jupyter_text>Learning on data with SPTo show the impact of SP on ML systems?<jupyter_code>import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import scipy.special as scisp
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn import mixture
import sklearn

import wiggum as wg
import sp_data_util as spdata
r_clusters = -.9  # correlation coefficient of clusters
cluster_spread = [.6,.8,.5] # pearson correlation of means
p_sp_clusters = .75 # portion of clusters with SP 
k = [3, 2,5] # number of clusters
cluster_size = [7,1]
domain_range = [0, 20, 0, 20]
N = 200 # number of points
p_clusters =  [[1.0/k_i]*k_i for k_i in k] 
n_views = 3

many_sp_df_diff = spdata.geometric_indep_views_gmm_sp(n_views,r_clusters,cluster_size,cluster_spread,p_sp_clusters,
                domain_range,k,N,p_clusters,numeric_categorical=True)

sp_design_list = [('x1','x2','A'),('x3','x4','B'), ('x5','x6','C')]
many_sp_df_diff.head()
spdata.sp_plot(many_sp_df_diff,'x1','x2','A')
spdata.sp_plot(many_sp_df_diff,'x3','x4','B')
spdata.sp_plot(many_sp_df_diff,'x5','x6','C')<jupyter_output><empty_output>
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