for m in M:
Pearson = []
Spearman = []
Pearson_p = []
Spearman_p = []
Pearson_MDS = []
Pearson_Iso = []
Pearson_MDS_p = []
Pearson_Iso_p = []
Pearson_svd = []
Spearman_svd = []
Cluster = []
Cluster_svd = []
for _ in range(iters):
Phi = random_phi(m, X.shape[0])
Y, noise = get_observations(X, Phi, snr=snr, return_noise=True)
pearson_dist, spearman_dist = compare_distances(X, Y, pvalues=True)
#cluster_similarity = compare_clusters(X, Y)
Pearson.append(pearson_dist[0])
#Spearman.append(spearman_dist[0])
#Pearson_p.append(pearson_dist[1])
#Spearman_p.append(spearman_dist[1])
#Cluster.append(cluster_similarity)
#X_mds = MDS().fit_transform(X.T).T
#Y_mds = MDS().fit_transform(Y.T).T
#X_iso = Isomap().fit_transform(X.T).T
#Y_iso = Isomap().fit_transform(Y.T).T
#pearson_mds, spearman_mds = compare_distances(X_mds, Y_mds, pvalues=True)
#pearson_iso, spearman_iso = compare_distances(X_iso, Y_iso, pvalues=True)
#Pearson_MDS.append(pearson_mds[0])
#Pearson_Iso.append(pearson_iso[0])
import numpy as np
from dl_simulation import random_phi, get_observations
from analyze_predictions import *
import glob,os
import sys
fp,snr = sys.argv[1:]
snr = float(snr)
iters = 15
prefix = fp[:fp.rfind('/')]
X = np.load(fp)
thresh = np.percentile(X,99.5)
X[(X > thresh)] = thresh
Cluster = []
for _ in range(iters):
Phi = np.eye(X.shape[0])
Y = get_observations(X,Phi,snr=snr)
cluster_similarity = compare_clusters(X,Y)
Cluster.append(cluster_similarity)
print(prefix,np.average(Cluster),np.std(Cluster))