Full working example of connecting Netezza from Java and python

Before start connecting you must make sure you can access the Netezza database and table from the machine where you are trying to run Java and or Python samples. Connecting Netezza server from Python Sample Check out my Ipython Jupyter Notebook with Python Sample Step 1: Importing python jaydebeapi library import jaydebeapi Step 2: Setting Database […]

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Python example of building GLM, GBM and Random Forest Binomial Model with H2O

Here is an example of using H2O machine learning library and then building GLM, GBM and Distributed Random Forest models for categorical response variable. Lets import h2o library and initialize the H2O machine learning cluster: import h2o h2o.init() Importing dataset and getting familiar with it: df = h2o.import_file(“https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate.csv”) df.summary() df.col_names Lets configure our predictors and […]

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RIP Theano

Before TensorFlow, PyTorch and Caffe; Theano was the major library for deep learning development. However, the library’s development and support will end after the upcoming Theano 1.0 release. The news came in an email from Theano’s main developer Pascal Lamblin and Yoshua Bengio, notable expert on artificial neural networks and deep learning. “We will continue […]

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Visualizing H2O GBM and Random Forest MOJO Models Trees in python

In this example we will build a tree based model first using H2O machine learning library and the save that model as MOJO. Using GraphViz/Dot library we will extract individual trees/cross validated model trees from the MOJO and visualize them. If you are new to H2O MOJO model, learn here. You can also get full […]

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H2O AutoML examples in python and Scala

AutoML is included into H2O version 3.14.0.1 and above. You can learn more about AutoML in the H2O blog here. H2O’s AutoML can be used for automating a large part of the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. The user can also use a performance […]

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OSGP: Create Chainage ticks along a Line at Specified Distance Intervals

This builds on from the previous post creating points at specified distances along a line. Here, we create perpendicular chainage ticks that traverse the main line. from osgeo import ogr from shapely.geometry import MultiLineString, LineString, Point from shapely import wkt import sys, math ## http://wikicode.wikidot.com/get-angle-of-line-between-two-points ## angle between two points def getAngle(pt1, pt2): x_diff = […]

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OSGP: Create Points at Specified Distance Interval Along a Line

This workflow with Python using OGR and Shapely creates points along a line at a specified distance interval. I use the FileGDB driver here to read from and write data to but you can change these to suit your requirements. The code is commented… from osgeo import ogr from shapely.geometry import MultiLineString, Point from shapely […]

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H2O backend and API processing through Rapids

H2O cluster support various frontend i.e. python, R, FLOW etc and all the functions at these various front ends are handled through H2O cluster backend through API. Frontend actions are translated into API and H2O backend handles these API through Rapid expressions. We will understand how these APIs are handled from backend. Lets Start H2O […]

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RACECAR: A Powerful Platform for Robotics Research and Teaching

Massachusetts Institute of Technology student autonomous robot project. Outline: Section 1:Introduction Section 2:Course Description Section 3:Images Section 4:Hardware Overview Section 5:Software OverView Section 6:Algorithm introduction Section 7:Conclusion Introduction The origin of the robotic mini-car race is an MIT’s robotics course called “Robotics: Science and Systems” (6.141/16.405). Its goal is teaching robotics with the RACECAR platforms. […]

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