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In general there are two types of problems to solve:
In pretty much all cases, all you need to do is have a way of looping through the array of numbers, keeping track of what you have done so far, and reporting the result at the end.
Alternate title: Cross compiling Windows/Darwin/Linux amd64/386/arm all from linux
After fumbling around trying to figure out the go toolchain and cross compilation configuration, I ran across the wiki page on Go's homepage. It's super helpful, and worked out of the box. I'm including the necessary scripts here in case they get changed or lost, and we can help Google find it (since it's the first real source I've found that "Just Worked"). http://code.google.com/p/go-wiki/wiki/WindowsCrossCompiling
I have a project that's a few years old, but needs some maintenance. Since I last worked on the project, two major things have happened. First, Elixir -- a declarative layer on top of SQLAlchemy -- has not been maintained. Secondly, SQLAlchemy now has its own declarative layer.
Rather than continue using Elixir, I decided to migrate my data models to use the new SQLAlchemy declarative layer, which interestingly enough appear to be relatively compatible with Elixir's philosophy.
The first thing I did was do a direct mapping in my model columns and update the import statements.
# from Elixir import Entity, Field, DateTime, Unicode, Integer, Boolean, setup_all, create_all, session, metadata from sqlalchemy import Column, DateTime, String, Integer, ForeignKey, Boolean from sqlalchemy.ext.declarative import declarative_base
Interested in becoming friendly with local crows after reading stories and first hand accounts. Obtained peanuts. Later, found grackles, but not crows. They're smaller and travel in larger flocks. Were not very interested in peanuts. They have irridescent blue heads. Curious if the small blue glass left in my garden is from the crows?
Squirrels like peanuts too. I threw a peanut towards an squirrel and it bounced off him. It did not phase him. He ate the peanut.
Jun 27 2017
Installed, via pip, the python bindings for tensorflow (for CPU, not GPU/CUDA)
sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl