Skip to content

Instantly share code, notes, and snippets.

Cam standarderror

Block or report user

Report or block standarderror

Hide content and notifications from this user.

Learn more about blocking users

Contact Support about this user’s behavior.

Learn more about reporting abuse

Report abuse
View GitHub Profile
@standarderror
standarderror / smear.sql
Created Aug 29, 2017
Smearing start-end date data
View smear.sql
Imagine you have some start-end data:
create table PRD_CAA_CRE_DDWSP_PI6_DPOL.TBL_DATA_1 as (
select ACCT_ID
, START_DATE
, END_DATE
, CURR_CRDT_LIM_AMT
from PRD_ADS_IL_VR.VR_S_ACCT_CRDT_CRD_RAW
sample 1000)
with data;
@standarderror
standarderror / SQL to R.txt
Created Apr 8, 2017
SQL equivalents in R
View SQL to R.txt
SELECT ... FROM a JOIN b WHERE ... GROUP BY ... HAVING ... ORDER BY ...
is equivalent to a chain of R commands involving
a %>%
select(...) %>%
filter(...) %>%
inner_join(b, ...) %>%
group_by(...) %>%
summarise(...) %>%
View 20161112_Neural_Style_TensorFlow.py
import os
import numpy as np
import scipy.misc
import scipy.io
import math
import tensorflow as tf
from sys import stderr
from functools import reduce
import time
View CNN: train CNN for cat dog data.py
"""
Based on the tflearn CIFAR-10 example at:
https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py
"""
from __future__ import division, print_function, absolute_import
from skimage import color, io
from scipy.misc import imresize
import numpy as np
View CNN: visualise conv layer outputs.py
# choose images & plot the first one
im = allX[102:103]
plt.axis('off')
plt.imshow(im[0].astype('uint8'))
plt.gcf().set_size_inches(2, 2)
# run images through 1st conv layer
m2 = tflearn.DNN(conv_1, session=model.session)
yhat = m2.predict(im)
@standarderror
standarderror / 20160215_Decision_boundaries.py
Last active Feb 16, 2016
20160215 Decision Boundaries
View 20160215_Decision_boundaries.py
### Prepeare python
%pylab inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import scipy.stats as stats
import neurolab as nl
from sklearn.datasets import make_classification
from sklearn import neighbors, tree, svm
@standarderror
standarderror / Autoencoder.py
Last active Sep 26, 2016
20150715 Autoencoder
View Autoencoder.py
### Autoencoder
###################################
## [1] Parameters & hyperparameters
###################################
# shape of data
N = shape(X)[1] # num observation
D = shape(X)[0] # num features (dimensionality)
@standarderror
standarderror / index.html
Created Jan 12, 2015
20150115 Regression Animation in Viewbox
View index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
#chart {
/* margin-left:50%; */
}
.circle {
stroke: '#fff';
@standarderror
standarderror / index.html
Last active Feb 17, 2016
20150111 Regression Scatterplot Animation
View index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
#chart {
}
.circle {
stroke: '#fff';
@standarderror
standarderror / d3.min.js
Last active Aug 29, 2015
20150107 Movies by genre history
View d3.min.js
!function(){function n(n,t){return t>n?-1:n>t?1:n>=t?0:0/0}function t(n){return null===n?0/0:+n}function e(n){return!isNaN(n)}function r(n){return{left:function(t,e,r,u){for(arguments.length<3&&(r=0),arguments.length<4&&(u=t.length);u>r;){var i=r+u>>>1;n(t[i],e)<0?r=i+1:u=i}return r},right:function(t,e,r,u){for(arguments.length<3&&(r=0),arguments.length<4&&(u=t.length);u>r;){var i=r+u>>>1;n(t[i],e)>0?u=i:r=i+1}return r}}}function u(n){return n.length}function i(n){for(var t=1;n*t%1;)t*=10;return t}function o(n,t){for(var e in t)Object.defineProperty(n.prototype,e,{value:t[e],enumerable:!1})}function a(){this._=Object.create(null)}function c(n){return(n+="")===da||n[0]===ma?ma+n:n}function l(n){return(n+="")[0]===ma?n.slice(1):n}function s(n){return c(n)in this._}function f(n){return(n=c(n))in this._&&delete this._[n]}function h(){var n=[];for(var t in this._)n.push(l(t));return n}function g(){var n=0;for(var t in this._)++n;return n}function p(){for(var n in this._)return!1;return!0}function v(){this._=Object
You can’t perform that action at this time.