how to build a good test bed?

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From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Skilgannon (talk)21:56, 3 October 2017

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Rsalesc (talk)22:07, 3 October 2017
 

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Rsalesc (talk)22:29, 3 October 2017

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Skilgannon (talk)10:10, 12 October 2017
 

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Xor (talk)02:33, 4 October 2017

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Rsalesc (talk)11:34, 4 October 2017
 

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Skilgannon (talk)10:09, 12 October 2017

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Xor (talk)15:05, 12 October 2017

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Skilgannon (talk)16:32, 12 October 2017

From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Xor (talk)01:49, 13 October 2017
 
 
 
 
 
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