Phil 6.22.17

Research 6:45 – 7:45

BRI 8:30 – 3:30

  • Working on reading in files, then running permutations on cluster membership
  • Because I can never remember how ass-backwards pandas.Dataframes are:
    def initDictSeries(rows = 3, cols = 5, offset=1, prefix ="doc_"):
        dict = {}
        row_names = []
        for j in range(cols):
            row_names.append("row_{0}".format(j))
        for i in range(rows):
            name = prefix+'{0}'.format(i)
            array = []
            for j in range(cols):
                array.append ((i+offset)*10 + j)
            #dict[name] = tf.Variable(np.random.rand(cols), tf.float32)
            dict[name] = array
        return pd.DataFrame(dict, index=row_names)
    
    df = initDictSeries()
    print("df = \n{0}".format(df))
    s = df.loc['row_1']
    print("\ns = df.loc['row_1'] = \n{0}".format(s))
    s['doc_1'] = 99
    print("modified = \n{0}".format(df))
  • Yay! It looks like it’s working. Next step is to run it. Done. This is the compare of the two runs on the older data as it sits on the servers: Oct_vs_Mar_2016_clusters
  • Here’s the same runs locally, using t-sne. This is more like what I was expecting to see: Oct_vs_Mar_local_2016_clusters
  • Here’s the old vs. new (Left this cooking. These charts take forever to calculate) Oct_2016_vs_June_2017_local_clusters
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