WIP: Boolean Network Randomization

.appveyor.yml

@@ -2,7 +2,6 @@ version: 0.1.0.{build}
 
 environment:
   matrix:
-    - PYTHON: "C:\\Python27-x64"
     - PYTHON: "C:\\Python35-x64"
     - PYTHON: "C:\\Python36-x64"
     - PYTHON: "C:\\Python37-x64"

.green

@@ -1,3 +1,4 @@
 verbose = 1
 logging = True
 run-coverage = True
+omit-patterns = .eggs/*

.travis.yml

@@ -7,7 +7,6 @@ os:
   # - osx
 
 python:
-  - "2.7"
   - "3.5"
   - "3.6"
   - "3.7"

examples/Tests.ipynb

@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0,\"../\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neet.boolean import WTNetwork\n",
+    "from os.path import join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neet.boolean.random import constraints, dynamics, randomizer, topology\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import test.tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tn = test.tests.TestNetwork()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_uniform_bias()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#tn.test_external_exclusion()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_correct_bias()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n",
+      "[((1, 2, 3), {'001', '100'}), ((1, 2, 3), {'011', '101'}), ((1, 2, 3), {'111'}), ((1, 2, 3), {'000', '010', '111'})]\n",
+      "bias_1:  8.0\n",
+      "row_count_1:  4.0\n",
+      "mean_1:  2.0\n",
+      "[((1, 2, 3), {'100', '000'}), ((1, 2, 3), {'011', '001'}), ((1, 2, 3), {'100', '010'}), ((1, 2, 3), {'110', '101'})]\n",
+      "bias_2:  8.0\n",
+      "row_count_2:  4.0\n",
+      "mean_2:  2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_correct_mean(tn.net4, printq=False, debug=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_correct_local()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_correct_errors_thrown()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/Untitled.ipynb

@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0,\"../\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neet.boolean import WTNetwork\n",
+    "from os.path import join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neet.boolean.random import constraints, dynamics, randomizer, topology\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import test.tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tn = test.tests.TestNetwork()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_uniform_bias()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#tn.test_external_exclusion()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_correct_bias()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n",
+      "[((1, 2, 3), {'001', '100'}), ((1, 2, 3), {'101', '011'}), ((1, 2, 3), {'111'}), ((1, 2, 3), {'111', '010', '000'})]\n",
+      "[((1, 2, 3), {'010', '000'}), ((1, 2, 3), {'111', '000'}), ((1, 2, 3), {'110', '010'}), ((1, 2, 3), {'011', '110'})]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tn.test_correct_mean(tn.net4, printq=False, debug=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

neet/boolean/__init__.py

@@ -19,3 +19,4 @@
 from .wtnetwork import WTNetwork  # noqa
 from .logicnetwork import LogicNetwork  # noqa
 from .sensitivity import SensitivityMixin  # noqa
+from . import random  # noqa

neet/boolean/random/__init__.py

@@ -0,0 +1,4 @@
+from . import constraints  # noqa
+from . import randomizer  # noqa
+from . import topology  # noqa
+from . import dynamics  # noqa