I hereby claim:
- I am calstad on github.
- I am calstad (https://keybase.io/calstad) on keybase.
- I have a public key ASALfeGf_mtOEUAF5VyKkgwcaKXhUuXafbGt-1mtsV99Jwo
To claim this, I am signing this object:
%matplotlib inline | |
import numpy as np | |
import pandas as pd | |
import matplotlib.cm as cm | |
import matplotlib.pyplot as plt | |
from sklearn import preprocessing, manifold |
df = pd.pd.read_pickle('claims.pickle') | |
encoded_df = pd.get_dummies(icd_df, columns=['npi', 'patient_id', 'icd_code']) |
scaler = preprocessing.StandardScaler().fit(encoded_df) | |
scaled_data = scaler.transform(encoded_df) |
# Assign a color to each ICD code for use in plots | |
icd_codes = icd_df.icd_code.unique() | |
icd_codes.sort() | |
colors = cm.rainbow(np.linspace(0,1,len(icd_codes))) | |
icd_colors = dict(zip(icd_codes, colors)) | |
row_colors = list() | |
for idx, row in icd_df.iterrows(): | |
row_icd = row['icd_code'] | |
row_color = icd_colors[row_icd] | |
row_colors.append(row_color) |
run_plot_tsne(dict(n_iter=10000, init='pca', learning_rate=500, perplexity=5)) |
I hereby claim:
To claim this, I am signing this object:
This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.
Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject
Mapper
algorithm.