Orbifold
/ KendoZeppelin.py
Created
November 17, 2016 07:54
Using Kendo inside a Zeppelin section is simple.
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| %angular | |
| <base href="http://demos.telerik.com/kendo-ui/gantt/index"> | |
| <style>html { font-size: 14px; font-family: Arial, Helvetica, sans-serif; }</style> | |
| <title></title> | |
| <link rel="stylesheet" href="http://kendo.cdn.telerik.com/2016.3.1028/styles/kendo.common-material.min.css" /> | |
| <link rel="stylesheet" href="http://kendo.cdn.telerik.com/2016.3.1028/styles/kendo.material.min.css" /> | |
| <link rel="stylesheet" href="http://kendo.cdn.telerik.com/2016.3.1028/styles/kendo.material.mobile.min.css" /> | |
| <script src="http://kendo.cdn.telerik.com/2016.3.1028/js/jquery.min.js"></script> |
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| # if needed, install plotly via shell like so | |
| %sh pip install plotly | |
| #--- | |
| %pyspark | |
| import plotly | |
| import numpy as np | |
| from plotly.graph_objs import Scatter, Layout | |
Orbifold
/ Forecasting.ipynb
Last active
April 19, 2024 17:33
A tutorial notebook on forecasting time series with R.
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Orbifold
/ SMOTE.ipynb
Created
November 12, 2016 15:44
Highlighting the usage of synthetic data (SMOTE) to imbalanced data.
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Orbifold
/ Chi2NoiseReduction.R
Created
November 11, 2016 06:36
Noise reduction using chi2. A synthetic proof by example.
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| probabilityInsideCluster = 0.5 | |
| probabilityOutsideCluster = 0.2 | |
| probabilityNoise = 0.3 | |
| clusterBoundaries = c(10, 25, 56, 81, 151, 293) | |
| clientTypeCount = c(0, 0, 0, 0) | |
| library(dplyr) | |
| library(rpart) | |
| # this create a frame of clients | |
| makeClientsFrame = function(attribCount = 1000, | |
| clientCount = 1000, |
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| library(stockPortfolio) | |
| library(quadprog) | |
| library(ggplot2) | |
| stocks <- c("SPY", "EFA", "IWM", "VWO", "LQD", "HYG") | |
| returns <- getReturns(stocks, freq = "week") | |
| eff.frontier <- function (returns, | |
| short = "no", | |
| max.allocation = NULL, |
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| require(devtools) | |
| install_version("imager", version = "0.20", repos = "http://cran.us.r-project.org") | |
| library("mxnet") | |
| library(imager) | |
| # can download the model from http://www.orbifold.net/default/R/MxNet/MxNetInception.zip | |
| model = mx.model.load("Inception_BN", iteration=39) | |
| mean.img = as.array(mx.nd.load("mean_224.nd")[["mean_img"]]) |
Orbifold
/ LinearRegressionThreeWays.ipynb
Created
November 4, 2016 06:12
Standard linear regression using TensorFlow, TFLearn and sklearn.
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Orbifold
/ PromisesSequence.html
Created
April 15, 2016 11:58
Chaining promising so they execute sequentially.
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| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>Title</title> | |
| </head> | |
| <body> | |
| <script type="text/javascript"> | |
| var getters = []; | |
| function getPromise(k) { |