tsne.py

import numpy as np
from sklearn.manifold import TSNE
import glob
import os
import sys
from numpy import genfromtxt
import matplotlib.pyplot as plt

numFilesRead = 0
data = np.zeros((144,1))

dataRows = []
dataFiles = []

def processData(fullPath) :
    global data
    global numFilesRead
    if ( numFilesRead > 10000) :
        return
    
    featureData = genfromtxt(fullPath, delimiter=',')
    dataRows.append(featureData)

    pathparts = fullPath.split("/")

    # dataFiles.append(pathparts[len(pathparts)-1].replace(".features",""))
    dataFiles.append(fullPath)
    numFilesRead += 1
 
        
for root, dirs, files in os.walk("."):
    if ( numFilesRead > 10000) :
        break
    path = root.split(os.sep)
    for fileName in files:
        if fileName.endswith(".features") :
            fullPath = root +"/"+ fileName
            processData(fullPath)
            
             
             
data = np.vstack(dataRows)
print(data)

X_embedded = TSNE(n_components=2).fit_transform(data)

for i in range(len(X_embedded)) :
    print(dataFiles[i] + "," + str(X_embedded[i][0]) + "," + str(X_embedded[i][1]))