From e1b4f2d580fcee3870c294b36c820305663b51f3 Mon Sep 17 00:00:00 2001 From: perceptualrobots Date: Fri, 20 Dec 2024 12:00:31 +0000 Subject: [PATCH] Refactor FunctionsData class and update hierarchy module imports and move examples to 51_pcthierarchy_examples --- nbs/01_putils.ipynb | 43 + nbs/04_hierarchy.ipynb | 1394 +-------------------------- nbs/16_environment_processing.ipynb | 6 +- nbs/51_pcthierarchy_examples.ipynb | 1280 ++++++++++++++++++++++++ pct/_modidx.py | 14 +- pct/hierarchy.py | 43 +- pct/putils.py | 96 +- 7 files changed, 1405 insertions(+), 1471 deletions(-) create mode 100644 nbs/51_pcthierarchy_examples.ipynb diff --git a/nbs/01_putils.ipynb b/nbs/01_putils.ipynb index b42d48fa..066ef7f1 100644 --- a/nbs/01_putils.ipynb +++ b/nbs/01_putils.ipynb @@ -387,6 +387,49 @@ " " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class FunctionsData():\n", + " \"Data collected for a set of functions\"\n", + " def __init__(self):\n", + " self.data = {}\n", + " \n", + " def add_data(self, func):\n", + " name = func.get_name()\n", + " if name in self.data.keys():\n", + " self.data[name].append(func.get_value())\n", + " else:\n", + " dlist=[]\n", + " self.data[name]=dlist\n", + " self.data[name].append(func.get_value())\n", + " \n", + " def add_reward(self, func):\n", + " name = 'reward'\n", + " if name in self.data.keys():\n", + " self.data[name].append(func.get_reward())\n", + " else:\n", + " dlist=[]\n", + " self.data[name]=dlist\n", + " self.data[name].append(func.get_reward())\n", + " \n", + " def add_fitness(self, func):\n", + " name = 'fitness'\n", + " if name in self.data.keys():\n", + " self.data[name].append(func.get_fitness())\n", + " else:\n", + " dlist=[]\n", + " self.data[name]=dlist\n", + " self.data[name].append(func.get_fitness())\n", + "\n", + " def add_list(self, key, list):\n", + " self.data[key]= list" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/nbs/04_hierarchy.ipynb b/nbs/04_hierarchy.ipynb index 80e61ca4..ef14d917 100644 --- a/nbs/04_hierarchy.ipynb +++ b/nbs/04_hierarchy.ipynb @@ -45,7 +45,8 @@ "#| export\n", "#import numpy as np\n", "from os import sep\n", - "import uuid\n" + "import uuid\n", + "import json\n" ] }, { @@ -61,7 +62,7 @@ "from pct.functions import BaseFunction, HPCTFUNCTION\n", "from pct.environments import EnvironmentFactory\n", "from pct.errors import BaseErrorCollector\n", - "from pct.putils import floatListsToString, PCTRunProperties\n" + "from pct.putils import floatListsToString, PCTRunProperties, FunctionsData\n" ] }, { @@ -74,70 +75,6 @@ "from pct.functions import Proportional" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class FunctionsData():\n", - " \"Data collected for a set of functions\"\n", - " def __init__(self):\n", - " self.data = {}\n", - " \n", - " def add_data(self, func):\n", - " name = func.get_name()\n", - " if name in self.data.keys():\n", - " self.data[name].append(func.get_value())\n", - " else:\n", - " dlist=[]\n", - " self.data[name]=dlist\n", - " self.data[name].append(func.get_value())\n", - " \n", - " def add_reward(self, func):\n", - " name = 'reward'\n", - " if name in self.data.keys():\n", - " self.data[name].append(func.get_reward())\n", - " else:\n", - " dlist=[]\n", - " self.data[name]=dlist\n", - " self.data[name].append(func.get_reward())\n", - " \n", - " def add_fitness(self, func):\n", - " name = 'fitness'\n", - " if name in self.data.keys():\n", - " self.data[name].append(func.get_fitness())\n", - " else:\n", - " dlist=[]\n", - " self.data[name]=dlist\n", - " self.data[name].append(func.get_fitness())\n", - "\n", - " def add_list(self, key, list):\n", - " self.data[key]= list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -1540,1325 +1477,6 @@ " return score, dfig, pfigs\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| gui\n", - "# history=True\n", - "# hierarchy, env = PCTHierarchy.load_from_file('testfiles/ARC/ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties', min=True, render=True, history=history)\n", - "# hierarchy.summary()\n", - "\n", - "# score, dfig, pfigs = PCTHierarchy.run_and_draw_hierarchy(hierarchy, env, draw_file=True, draw_figsize=(5,5), history = history, plots=\"scEdges,scZero\", steps=50)#, draw_file='/tmp/tmp.png')\n", - "# print('Test score =',score)\n", - "# dfig\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| gui\n", - "\n", - "# for pfig in pfigs:\n", - "# display(pfig)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a Hierarchy\n", - "\n", - "Create a hierarchy by defining the number of rows (levels) and columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pct.functions import Constant" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ]]" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pre=Constant(5, name='precon')\n", - "namespace=pre.namespace\n", - "post=Constant(10, name='postcon', namespace=namespace)\n", - "hpct = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, clear_names=False, links=\"dense\", namespace=namespace)\n", - "hpct.hierarchy\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**************************\n", - "PRE: 5.000 \n", - "L0C0 0.000 0.000 0.000 0.000 \n", - "L0C1 0.000 0.000 0.000 0.000 \n", - "L0C2 0.000 0.000 0.000 0.000 \n", - "L1C0 0.000 0.000 0.000 0.000 \n", - "L1C1 0.000 0.000 0.000 0.000 \n", - "L1C2 0.000 0.000 0.000 0.000 \n", - "L2C0 0.000 0.000 0.000 0.000 \n", - "L2C1 0.000 0.000 0.000 0.000 \n", - "L2C2 0.000 0.000 0.000 0.000 \n", - "POST: 10.000 \n", - "\n" - ] - } - ], - "source": [ - "print(hpct.get_summary())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[[5]], [[10]]], [[[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]]], [[[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]]], [[[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]]]]\n" - ] - } - ], - "source": [ - "print(hpct.get_parameters_list())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3, 3, 3]\n" - ] - } - ], - "source": [ - "print(hpct.get_grid())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hpct.change_namespace()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**************************\n", - "pcthierarchy PCTHierarchy [3, 3, 3] b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "--------------------------\n", - "PRE: precon Constant | 5 \n", - "Level 0 Cols 3\n", - "level0col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", - "PER: variable Variable | 0 \n", - "COM: subtract Subtract | 0 | links weighted_sum variable \n", - "OUT: proportional Proportional | gain 1 | 0 | links subtract \n", - "----------------------------\n", - "level0col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", - "PER: variable1 Variable | 0 \n", - "COM: subtract1 Subtract | 0 | links weighted_sum1 variable1 \n", - "OUT: proportional1 Proportional | gain 1 | 0 | links subtract1 \n", - "----------------------------\n", - "level0col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", - "PER: variable2 Variable | 0 \n", - "COM: subtract2 Subtract | 0 | links weighted_sum2 variable2 \n", - "OUT: proportional2 Proportional | gain 1 | 0 | links subtract2 \n", - "----------------------------\n", - "Level 1 Cols 3\n", - "level1col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract3 Subtract | 0 | links weighted_sum4 weighted_sum3 \n", - "OUT: proportional3 Proportional | gain 1 | 0 | links subtract3 \n", - "----------------------------\n", - "level1col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract4 Subtract | 0 | links weighted_sum6 weighted_sum5 \n", - "OUT: proportional4 Proportional | gain 1 | 0 | links subtract4 \n", - "----------------------------\n", - "level1col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract5 Subtract | 0 | links weighted_sum8 weighted_sum7 \n", - "OUT: proportional5 Proportional | gain 1 | 0 | links subtract5 \n", - "----------------------------\n", - "Level 2 Cols 3\n", - "level2col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant Constant | 0 \n", - "PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract6 Subtract | 0 | links constant weighted_sum9 \n", - "OUT: proportional6 Proportional | gain 1 | 0 | links subtract6 \n", - "----------------------------\n", - "level2col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant1 Constant | 0 \n", - "PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract7 Subtract | 0 | links constant1 weighted_sum10 \n", - "OUT: proportional7 Proportional | gain 1 | 0 | links subtract7 \n", - "----------------------------\n", - "level2col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant2 Constant | 0 \n", - "PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract8 Subtract | 0 | links constant2 weighted_sum11 \n", - "OUT: proportional8 Proportional | gain 1 | 0 | links subtract8 \n", - "----------------------------\n", - "POST: postcon Constant | 10 \n", - "**************************\n" - ] - } - ], - "source": [ - "hpct.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#FunctionsList.getInstance().report() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a hierarchy from a configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'precon', 'value': 5, 'links': {}}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level0col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'weighted_sum', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level0col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum1', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable1', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'weighted_sum1', 1: 'variable1'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level0col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable2', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'weighted_sum2', 1: 'variable2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 1}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level1col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum4', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum3', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract3', 'value': 0, 'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional3', 'value': 0, 'links': {0: 'subtract3'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level1col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum6', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum5', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract4', 'value': 0, 'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional4', 'value': 0, 'links': {0: 'subtract4'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level1col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum8', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum7', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract5', 'value': 0, 'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional5', 'value': 0, 'links': {0: 'subtract5'}, 'gain': 1}}}}}}, 'level2': {'level': 2, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level2col0', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum9', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract6', 'value': 0, 'links': {0: 'constant', 1: 'weighted_sum9'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional6', 'value': 0, 'links': {0: 'subtract6'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level2col1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum10', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract7', 'value': 0, 'links': {0: 'constant1', 1: 'weighted_sum10'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional7', 'value': 0, 'links': {0: 'subtract7'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level2col2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum11', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract8', 'value': 0, 'links': {0: 'constant2', 1: 'weighted_sum11'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional8', 'value': 0, 'links': {0: 'subtract8'}, 'gain': 1}}}}}}}, 'post': {'post0': {'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}}}\n" - ] - } - ], - "source": [ - "config = hpct.get_config()\n", - "print(config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# h = PCTHierarchy.from_config(config, namespace=namespace)\n", - "h = PCTHierarchy.from_config(config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert h.get_config() == hpct.get_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Viewing a Hierarchy\n", - "\n", - "The hierarchy details can be viewed as a summary. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**************************\n", - "pcthierarchy PCTHierarchy [3, 3, 3] b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "--------------------------\n", - "PRE: precon Constant | 5 \n", - "Level 0 Cols 3\n", - "level0col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", - "PER: variable Variable | 0 \n", - "COM: subtract Subtract | 0 | links weighted_sum variable \n", - "OUT: proportional Proportional | gain 10 | 0 | links subtract \n", - "----------------------------\n", - "level0col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", - "PER: variable1 Variable | 0 \n", - "COM: subtract1 Subtract | 0 | links weighted_sum1 variable1 \n", - "OUT: proportional1 Proportional | gain 10 | 0 | links subtract1 \n", - "----------------------------\n", - "level0col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", - "PER: variable2 Variable | 0 \n", - "COM: subtract2 Subtract | 0 | links weighted_sum2 variable2 \n", - "OUT: proportional2 Proportional | gain 10 | 0 | links subtract2 \n", - "----------------------------\n", - "Level 1 Cols 3\n", - "level1col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract3 Subtract | 0 | links weighted_sum4 weighted_sum3 \n", - "OUT: proportional3 Proportional | gain 10 | 0 | links subtract3 \n", - "----------------------------\n", - "level1col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract4 Subtract | 0 | links weighted_sum6 weighted_sum5 \n", - "OUT: proportional4 Proportional | gain 10 | 0 | links subtract4 \n", - "----------------------------\n", - "level1col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract5 Subtract | 0 | links weighted_sum8 weighted_sum7 \n", - "OUT: proportional5 Proportional | gain 10 | 0 | links subtract5 \n", - "----------------------------\n", - "Level 2 Cols 3\n", - "level2col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant Constant | 1 \n", - "PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract6 Subtract | 0 | links constant weighted_sum9 \n", - "OUT: proportional6 Proportional | gain 10 | 0 | links subtract6 \n", - "----------------------------\n", - "level2col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant1 Constant | 1 \n", - "PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract7 Subtract | 0 | links constant1 weighted_sum10 \n", - "OUT: proportional7 Proportional | gain 10 | 0 | links subtract7 \n", - "----------------------------\n", - "level2col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant2 Constant | 1 \n", - "PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract8 Subtract | 0 | links constant2 weighted_sum11 \n", - "OUT: proportional8 Proportional | gain 10 | 0 | links subtract8 \n", - "----------------------------\n", - "POST: postcon Constant | 10 \n", - "**************************\n" - ] - } - ], - "source": [ - "hpct.get_node(2,0).get_function('reference').set_value(1)\n", - "hpct.get_node(2,1).get_function('reference').set_value(1)\n", - "hpct.get_node(2,2).get_function('reference').set_value(1)\n", - "for level in range(3):\n", - " for col in range(3):\n", - " hpct.get_node(level,col).get_function('output').set_property('gain', 10)\n", - "hpct.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The hierarchy details can be viewed as a configuration. That configuration can be used to create a hierarchy, as shown above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'PCTHierarchy',\n", - " 'name': 'pcthierarchy',\n", - " 'pre': {'pre0': {'type': 'Constant',\n", - " 'name': 'precon',\n", - " 'value': 5,\n", - " 'links': {}}},\n", - " 'levels': {'level0': {'level': 0,\n", - " 'nodes': {'col0': {'col': 0,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level0col0',\n", - " 'refcoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum',\n", - " 'value': 0,\n", - " 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'percoll': {'0': {'type': 'Variable',\n", - " 'name': 'variable',\n", - " 'value': 0,\n", - " 'links': {}}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum', 1: 'variable'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract'},\n", - " 'gain': 10}}}},\n", - " 'col1': {'col': 1,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level0col1',\n", - " 'refcoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum1',\n", - " 'value': 0,\n", - " 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'percoll': {'0': {'type': 'Variable',\n", - " 'name': 'variable1',\n", - " 'value': 0,\n", - " 'links': {}}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract1',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum1', 1: 'variable1'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional1',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract1'},\n", - " 'gain': 10}}}},\n", - " 'col2': {'col': 2,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level0col2',\n", - " 'refcoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum2',\n", - " 'value': 0,\n", - " 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'percoll': {'0': {'type': 'Variable',\n", - " 'name': 'variable2',\n", - " 'value': 0,\n", - " 'links': {}}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract2',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum2', 1: 'variable2'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional2',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract2'},\n", - " 'gain': 10}}}}}},\n", - " 'level1': {'level': 1,\n", - " 'nodes': {'col0': {'col': 0,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level1col0',\n", - " 'refcoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum4',\n", - " 'value': 0,\n", - " 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'percoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum3',\n", - " 'value': 0,\n", - " 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract3',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional3',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract3'},\n", - " 'gain': 10}}}},\n", - " 'col1': {'col': 1,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level1col1',\n", - " 'refcoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum6',\n", - " 'value': 0,\n", - " 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'percoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum5',\n", - " 'value': 0,\n", - " 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract4',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional4',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract4'},\n", - " 'gain': 10}}}},\n", - " 'col2': {'col': 2,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level1col2',\n", - " 'refcoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum8',\n", - " 'value': 0,\n", - " 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'percoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum7',\n", - " 'value': 0,\n", - " 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract5',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional5',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract5'},\n", - " 'gain': 10}}}}}},\n", - " 'level2': {'level': 2,\n", - " 'nodes': {'col0': {'col': 0,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level2col0',\n", - " 'refcoll': {'0': {'type': 'Constant',\n", - " 'name': 'constant',\n", - " 'value': 1,\n", - " 'links': {}}},\n", - " 'percoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum9',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract6',\n", - " 'value': 0,\n", - " 'links': {0: 'constant', 1: 'weighted_sum9'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional6',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract6'},\n", - " 'gain': 10}}}},\n", - " 'col1': {'col': 1,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level2col1',\n", - " 'refcoll': {'0': {'type': 'Constant',\n", - " 'name': 'constant1',\n", - " 'value': 1,\n", - " 'links': {}}},\n", - " 'percoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum10',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract7',\n", - " 'value': 0,\n", - " 'links': {0: 'constant1', 1: 'weighted_sum10'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional7',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract7'},\n", - " 'gain': 10}}}},\n", - " 'col2': {'col': 2,\n", - " 'node': {'type': 'PCTNode',\n", - " 'name': 'level2col2',\n", - " 'refcoll': {'0': {'type': 'Constant',\n", - " 'name': 'constant2',\n", - " 'value': 1,\n", - " 'links': {}}},\n", - " 'percoll': {'0': {'type': 'WeightedSum',\n", - " 'name': 'weighted_sum11',\n", - " 'value': 0,\n", - " 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n", - " 'weights': [1, 1, 1]}},\n", - " 'comcoll': {'0': {'type': 'Subtract',\n", - " 'name': 'subtract8',\n", - " 'value': 0,\n", - " 'links': {0: 'constant2', 1: 'weighted_sum11'}}},\n", - " 'outcoll': {'0': {'type': 'Proportional',\n", - " 'name': 'proportional8',\n", - " 'value': 0,\n", - " 'links': {0: 'subtract8'},\n", - " 'gain': 10}}}}}}},\n", - " 'post': {'post0': {'type': 'Constant',\n", - " 'name': 'postcon',\n", - " 'value': 10,\n", - " 'links': {}}}}" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hpct.get_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the output function, which will be the output function of the last node, or the last item of the post-processor functions, if present." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}\n" - ] - } - ], - "source": [ - "link = hpct.get_output_function()\n", - "print(link.get_config())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The hierarhcy can also be viewed graphically as a network of connected nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ryoung\\AppData\\Local\\Temp\\ipykernel_988\\790717467.py:277: UserWarning:\n", - "\n", - "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - "\n" - ] - } - ], - "source": [ - "ahpct = PCTHierarchy(2,2, links=\"dense\")\n", - "\n", - "test = 3\n", - "if test==1:\n", - " g = ahpct.graph()\n", - " pos=graphviz_layout(g, prog='dot')\n", - " nx.draw(g, pos=pos, with_labels=True, font_size=12, font_weight='bold', node_color='red', node_size=500)\n", - "\n", - "if test ==2:\n", - " g = ahpct.graph()\n", - " pos = nx.multipartite_layout(g, subset_key=\"layer\", align='horizontal')\n", - " pos['constant1'][0]+=0.2\n", - " c = pos['constant1'][0]\n", - " print(c)\n", - " nx.draw(g, pos=pos, with_labels=True, font_weight='bold', node_color='red', node_size=750, arrowsize=25)\n", - "\n", - "if test ==3:\n", - " if os.name=='nt': \n", - " ahpct.draw(file=\"ahpct.png\", node_size=1500, figsize=(10,10))# with_labels=True, font_weight='bold', node_color='red', node_size=500, arrowsize=25, align='vertical'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running a hierarchy\n", - "\n", - "The hierachy can be run once by calling itself. The verbose flag will print the computations to the screen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5.000 \n", - "level0col0 0.000 0.000 0.000 0.000 \n", - "level0col1 0.000 0.000 0.000 0.000 \n", - "level0col2 0.000 0.000 0.000 0.000 \n", - "level1col0 0.000 0.000 0.000 0.000 \n", - "level1col1 0.000 0.000 0.000 0.000 \n", - "level1col2 0.000 0.000 0.000 0.000 \n", - "level2col0 1.000 0.000 1.000 10.000 \n", - "level2col1 1.000 0.000 1.000 10.000 \n", - "level2col2 1.000 0.000 1.000 10.000 \n", - "10.000 \n" - ] - }, - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hpct(verbose=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A hierarchy can be executed with the \"run()\" method, providing the number of iterations to run. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hpct1 = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, links=\"dense\")\n", - "namespace=hpct1.namespace\n", - "hpct1.get_node(2,0).get_function('reference').set_value(1)\n", - "hpct1.get_node(2,1).get_function('reference').set_value(1)\n", - "hpct1.get_node(2,2).get_function('reference').set_value(1)\n", - "for level in range(3):\n", - " for col in range(3):\n", - " hpct1.get_node(level,col).get_function('output').set_property('gain', 10)\n", - "\n", - "hpct1.run(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Viewing Data\n", - "\n", - "If the hierarchy is created with the \"history\" flag equal to True, the data can be retrieved for each node. The node is accessed by specifying the row and column within the hierarchy. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'refcoll': {'weighted_sum6': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'percoll': {'weighted_sum5': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 'comcoll': {'subtract4': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'outcoll': {'proportional4': [0, 300, 300, 300, 300, 300, 300, 300, 300, 300]}}\n" - ] - } - ], - "source": [ - "print(hpct1.get_node(1,1).history.data)\n", - "assert hpct1.get_node(1,1).history.data == {'refcoll': {'weighted_sum6': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'percoll': {'weighted_sum5': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'comcoll': {'subtract4': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'outcoll': {'proportional4': [0.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0]}}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save and Load" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save a hierarchy to file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hpct1.save(\"hpct.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a hierarchy from file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**************************\n", - "pcthierarchy PCTHierarchy [3, 3, 3] b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "--------------------------\n", - "PRE: precon Constant | 5 \n", - "Level 0 Cols 3\n", - "level0col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum WeightedSum | weights [1, 1, 1] | 900 | links proportional3 proportional4 proportional5 \n", - "PER: variable Variable | 0 \n", - "COM: subtract Subtract | 900 | links weighted_sum variable \n", - "OUT: proportional Proportional | gain 10 | 9000 | links subtract \n", - "----------------------------\n", - "level0col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 900 | links proportional3 proportional4 proportional5 \n", - "PER: variable1 Variable | 0 \n", - "COM: subtract1 Subtract | 900 | links weighted_sum1 variable1 \n", - "OUT: proportional1 Proportional | gain 10 | 9000 | links subtract1 \n", - "----------------------------\n", - "level0col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 900 | links proportional3 proportional4 proportional5 \n", - "PER: variable2 Variable | 0 \n", - "COM: subtract2 Subtract | 900 | links weighted_sum2 variable2 \n", - "OUT: proportional2 Proportional | gain 10 | 9000 | links subtract2 \n", - "----------------------------\n", - "Level 1 Cols 3\n", - "level1col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 30 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract3 Subtract | 30 | links weighted_sum4 weighted_sum3 \n", - "OUT: proportional3 Proportional | gain 10 | 300 | links subtract3 \n", - "----------------------------\n", - "level1col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 30 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract4 Subtract | 30 | links weighted_sum6 weighted_sum5 \n", - "OUT: proportional4 Proportional | gain 10 | 300 | links subtract4 \n", - "----------------------------\n", - "level1col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 30 | links proportional6 proportional7 proportional8 \n", - "PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", - "COM: subtract5 Subtract | 30 | links weighted_sum8 weighted_sum7 \n", - "OUT: proportional5 Proportional | gain 10 | 300 | links subtract5 \n", - "----------------------------\n", - "Level 2 Cols 3\n", - "level2col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant Constant | 1 \n", - "PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract6 Subtract | 1 | links constant weighted_sum9 \n", - "OUT: proportional6 Proportional | gain 10 | 10 | links subtract6 \n", - "----------------------------\n", - "level2col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant1 Constant | 1 \n", - "PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract7 Subtract | 1 | links constant1 weighted_sum10 \n", - "OUT: proportional7 Proportional | gain 10 | 10 | links subtract7 \n", - "----------------------------\n", - "level2col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant2 Constant | 1 \n", - "PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", - "COM: subtract8 Subtract | 1 | links constant2 weighted_sum11 \n", - "OUT: proportional8 Proportional | gain 10 | 10 | links subtract8 \n", - "----------------------------\n", - "POST: postcon Constant | 10 \n", - "**************************\n" - ] - } - ], - "source": [ - "#loaded = PCTHierarchy.load(\"hpct.json\", clear=False, namespace=namespace)\n", - "loaded = PCTHierarchy.load(\"hpct.json\", clear=False)\n", - "loaded.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import networkx as nx\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend agg is non-interactive backend. Turning interactive mode off.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# https://matplotlib.org/3.1.0/gallery/color/named_colors.html\n", - "loaded.draw(with_edge_labels=True, color_mapping={'w':'aqua','c':'limegreen','s':'goldenrod', 'p':'red', 'v':'silver'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Examples\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Build a hierarchy by adding nodes and functions manually.\n", - "\n", - "Create an empty hierarchy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**************************\n", - "pcthierarchy PCTHierarchy [] b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "--------------------------\n", - "PRE: None\n", - "POST: None\n", - "**************************\n" - ] - } - ], - "source": [ - "myhpct = PCTHierarchy()\n", - "namespace=myhpct.namespace\n", - "myhpct.summary(build=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add a node. Then nodes at particular positions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**************************\n", - "pcthierarchy PCTHierarchy [2, 1] b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "--------------------------\n", - "PRE: None\n", - "Level 0 Cols 2\n", - "pctnode2 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant2 Constant | 0 \n", - "PER: variable2 Variable | 0 \n", - "COM: subtract2 Subtract | 0 \n", - "OUT: proportional2 Proportional | gain 1 | 0 \n", - "----------------------------\n", - "pctnode PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant Constant | 0 \n", - "PER: variable Variable | 0 \n", - "COM: subtract Subtract | 0 \n", - "OUT: proportional Proportional | gain 1 | 0 \n", - "----------------------------\n", - "Level 1 Cols 1\n", - "pctnode1 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant1 Constant | 0 \n", - "PER: variable1 Variable | 0 \n", - "COM: subtract1 Subtract | 0 \n", - "OUT: proportional1 Proportional | gain 1 | 0 \n", - "----------------------------\n", - "POST: None\n", - "**************************\n" - ] - } - ], - "source": [ - "myhpct.add_node(PCTNode(namespace=namespace))\n", - "myhpct.add_node(PCTNode(namespace=namespace), level=1)\n", - "myhpct.add_node(PCTNode(namespace=namespace), level=0)\n", - "myhpct.summary(build=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Replace functions at particular positions in the hierarchy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myhpct.insert_function(level=0, col=0, collection=\"perception\", function=Proportional(3, name=\"prop2\", namespace=namespace))\n", - "myhpct.insert_function(level=1, col=0, collection=\"perception\", function=WeightedSum(weights=[1,1], name=\"wsum\", namespace=namespace))\n", - "myhpct.insert_function(level=0, col=1, collection=\"reference\", function=Proportional(1, name=\"passthru\", namespace=namespace))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add pre and post processor functions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myhpct.add_preprocessor(Constant(1, name=\"cons1\", namespace=namespace))\n", - "myhpct.add_preprocessor(Proportional(5, name=\"prop1\", namespace=namespace))\n", - "myhpct.add_postprocessor(Proportional(5, name=\"postprop1\", namespace=namespace))\n", - "myhpct.add_postprocessor(Proportional(5, name=\"postprop2\", namespace=namespace))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Link the functions together." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myhpct.set_links(\"prop1\", \"cons1\")\n", - "myhpct.set_links(\"prop2\", \"prop1\")\n", - "myhpct.add_links(\"wsum\", \"prop2\", \"variable\")\n", - "myhpct.set_links(\"passthru\", \"proportional1\")\n", - "myhpct.set_links(\"postprop1\", \"proportional\")\n", - "myhpct.set_links(\"postprop2\", \"postprop1\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**************************\n", - "pcthierarchy PCTHierarchy [2, 1] b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "--------------------------\n", - "PRE: cons1 Constant | 1 \n", - "prop1 Proportional | gain 5 | 0 | links cons1 \n", - "Level 0 Cols 2\n", - "pctnode2 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant2 Constant | 1 \n", - "PER: prop2 Proportional | gain 3 | 0 | links prop1 \n", - "COM: subtract2 Subtract | 0 | links constant2 prop2 \n", - "OUT: proportional2 Proportional | gain 10 | 0 | links subtract2 \n", - "----------------------------\n", - "pctnode PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "----------------------------\n", - "REF: passthru Proportional | gain 1 | 0 | links proportional1 \n", - "PER: variable Variable | 0 \n", - "COM: subtract Subtract | 0 | links passthru variable \n", - "OUT: proportional Proportional | gain 10 | 0 | links subtract \n", - "----------------------------\n", - "Level 1 Cols 1\n", - "pctnode1 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n", - "----------------------------\n", - "REF: constant1 Constant | 1 \n", - "PER: wsum WeightedSum | weights [1, 1] | 0 | links prop2 variable \n", - "COM: subtract1 Subtract | 0 | links constant1 wsum \n", - "OUT: proportional1 Proportional | gain 10 | 0 | links subtract1 \n", - "----------------------------\n", - "POST: postprop1 Proportional | gain 5 | 0 | links proportional \n", - "postprop2 Proportional | gain 5 | 0 | links postprop1 \n", - "**************************\n" - ] - } - ], - "source": [ - "myhpct.get_node(0,0).get_function('reference').set_value(1)\n", - "myhpct.get_node(1,0).get_function('reference').set_value(1)\n", - "myhpct.get_node(0,0).get_function('output').set_property('gain', 10)\n", - "myhpct.get_node(0,1).get_function('output').set_property('gain', 10)\n", - "myhpct.get_node(1,0).get_function('output').set_property('gain', 10)\n", - "myhpct.summary(build=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myhpctconfig = myhpct.get_config()\n", - "#print(myhpctconfig)\n", - "assert myhpctconfig == {'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'cons1', 'value': 1, 'links': {}}, 'pre1': {'type': 'Proportional', 'name': 'prop1', 'value': 0, 'links': {0: 'cons1'}, 'gain': 5}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'Proportional', 'name': 'prop2', 'value': 0, 'links': {0: 'prop1'}, 'gain': 3}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'constant2', 1: 'prop2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 10}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'pctnode', 'refcoll': {'0': {'type': 'Proportional', 'name': 'passthru', 'value': 0, 'links': {0: 'proportional1'}, 'gain': 1}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'passthru', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'wsum', 'value': 0, 'links': {0: 'prop2', 1: 'variable'}, 'weights': [1.0, 1.0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'constant1', 1: 'wsum'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 10}}}}}}}, 'post': {'post0': {'type': 'Proportional', 'name': 'postprop1', 'value': 0, 'links': {0: 'proportional'}, 'gain': 5}, 'post1': {'type': 'Proportional', 'name': 'postprop2', 'value': 0, 'links': {0: 'postprop1'}, 'gain': 5}}}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define the order in which the node will be processed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myhpct.set_order([\"pctnode2\", \"pctnode1\", \"pctnode\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the hierarchy once." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.000 5.000 \n", - "pctnode2 1.000 15.000 -14.000 -140.000 \n", - "pctnode1 1.000 15.000 -14.000 -140.000 \n", - "pctnode -140.000 0.000 -140.000 -1400.000 \n", - "-7000.000 -35000.000 \n", - "-35000\n" - ] - } - ], - "source": [ - "out = myhpct(verbose=True)\n", - "print(out)\n", - "assert out == -35000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# config = {'type': 'Individual', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'CartPoleV1', 'name': 'CartPoleV1', 'value': [0.03498833197860944, 0.20994561633454428, 0.012668159509212712, -0.2705237130920193, 0.047656152654718356], 'links': {0: 'Action1'}, 'env_name': 'CartPole-v1', 'reward': 1.0, 'done': False, 'info': {}}, 'pre1': {'type': 'IndexedParameter', 'name': 'ICV', 'value': 0.20994561633454428, 'links': {0: 'CartPoleV1'}, 'index': 1}, 'pre2': {'type': 'IndexedParameter', 'name': 'ICP', 'value': 0.03498833197860944, 'links': {0: 'CartPoleV1'}, 'index': 0}, 'pre3': {'type': 'IndexedParameter', 'name': 'IPV', 'value': -0.2705237130920193, 'links': {0: 'CartPoleV1'}, 'index': 3}, 'pre4': {'type': 'IndexedParameter', 'name': 'IPA', 'value': 0.012668159509212712, 'links': {0: 'CartPoleV1'}, 'index': 2}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'L0C0', 'refcoll': {'0': {'type': 'EAConstant', 'name': 'RL0C0', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'EAWeightedSum', 'name': 'PL0C0', 'value': -0.2705237130920193, 'links': {0: 'ICV', 1: 'ICP', 2: 'IPV', 3: 'IPA'}, 'weights': [0, 0, 1, 0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'CL0C0', 'value': 0.2705237130920193, 'links': {0: 'RL0C0', 1: 'PL0C0'}}}, 'outcoll': {'0': {'type': 'EAProportional', 'name': 'OL0C0', 'value': -0.05046166000036782, 'links': {0: 'CL0C0'}, 'gain': -0.1865332226280776}}}}}}}, 'post': {'post0': {'type': 'EAWeightedSum', 'name': 'Action1', 'value': -0.005282911840894066, 'links': {0: 'OL0C0'}, 'weights': [0.10469159835121472]}}}\n", - "# ind = PCTHierarchy.from_config(config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TotalError limit:250, limit_exceeded:False, : RootSumSquaredError error_response:2.23606797749979\n", - "[0] \n", - "level0col0 0.000 0.000 0.000 0.000 \n", - "\n", - "Current score=2.23606797749979\n", - "[1] \n", - "level0col0 0.000 0.000 0.000 0.000 \n", - "\n", - "Current score=2.23606797749979\n", - "[2] \n", - "level0col0 0.000 0.000 0.000 0.000 \n", - "\n", - "Current score=2.23606797749979\n", - "[3] \n", - "level0col0 0.000 0.000 0.000 0.000 \n", - "\n", - "Current score=2.23606797749979\n", - "[4] \n", - "level0col0 0.000 0.000 0.000 0.000 \n", - "\n", - "Current score=2.23606797749979\n", - "2.23606797749979\n" - ] - } - ], - "source": [ - "from pct.errors import RootSumSquaredError, TotalError\n", - "\n", - "er = RootSumSquaredError()\n", - "te = TotalError(error_response=er, limit=250,min=True) \n", - "te.add_error_data([1, 2])\n", - "print(te)\n", - "\n", - "\n", - "hpct = PCTHierarchy(1,1,error_collector=te)\n", - "hpct.run(steps=5, verbose=True)\n", - "\n", - "\n", - "err=te.error()\n", - "print(err)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -2872,13 +1490,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.9.11" } }, "nbformat": 4, diff --git a/nbs/16_environment_processing.ipynb b/nbs/16_environment_processing.ipynb index 3e3d9041..333e553a 100644 --- a/nbs/16_environment_processing.ipynb +++ b/nbs/16_environment_processing.ipynb @@ -678,13 +678,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "python3", "language": "python", "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.9.11" } }, "nbformat": 4, diff --git a/nbs/51_pcthierarchy_examples.ipynb b/nbs/51_pcthierarchy_examples.ipynb new file mode 100644 index 00000000..ae6923fd --- /dev/null +++ b/nbs/51_pcthierarchy_examples.ipynb @@ -0,0 +1,1280 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "output-file: pcthierarchy_examples.html\n", + "title: PCTHierarchy examples\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pct.nodes import PCTNode\n", + "from pct.functions import WeightedSum, Constant\n", + "from pct.hierarchy import PCTHierarchy\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| include: false\n", + "from pct.functions import Proportional" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a Hierarchy\n", + "\n", + "Create a hierarchy by defining the number of rows (levels) and columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ]]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pre=Constant(5, name='precon')\n", + "namespace=pre.namespace\n", + "post=Constant(10, name='postcon', namespace=namespace)\n", + "hpct = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, clear_names=False, links=\"dense\", namespace=namespace)\n", + "hpct.hierarchy\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**************************\n", + "PRE: 5.000 \n", + "L0C0 0.000 0.000 0.000 0.000 \n", + "L0C1 0.000 0.000 0.000 0.000 \n", + "L0C2 0.000 0.000 0.000 0.000 \n", + "L1C0 0.000 0.000 0.000 0.000 \n", + "L1C1 0.000 0.000 0.000 0.000 \n", + "L1C2 0.000 0.000 0.000 0.000 \n", + "L2C0 0.000 0.000 0.000 0.000 \n", + "L2C1 0.000 0.000 0.000 0.000 \n", + "L2C2 0.000 0.000 0.000 0.000 \n", + "POST: 10.000 \n", + "\n" + ] + } + ], + "source": [ + "print(hpct.get_summary())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[[5]], [[10]]], [[[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]]], [[[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]]], [[[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]]]]\n" + ] + } + ], + "source": [ + "print(hpct.get_parameters_list())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, 3, 3]\n" + ] + } + ], + "source": [ + "print(hpct.get_grid())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hpct.change_namespace()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**************************\n", + "pcthierarchy PCTHierarchy [3, 3, 3] a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "--------------------------\n", + "PRE: precon Constant | 5 \n", + "Level 0 Cols 3\n", + "level0col0 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", + "PER: variable Variable | 0 \n", + "COM: subtract Subtract | 0 | links weighted_sum variable \n", + "OUT: proportional Proportional | gain 1 | 0 | links subtract \n", + "----------------------------\n", + "level0col1 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", + "PER: variable1 Variable | 0 \n", + "COM: subtract1 Subtract | 0 | links weighted_sum1 variable1 \n", + "OUT: proportional1 Proportional | gain 1 | 0 | links subtract1 \n", + "----------------------------\n", + "level0col2 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", + "PER: variable2 Variable | 0 \n", + "COM: subtract2 Subtract | 0 | links weighted_sum2 variable2 \n", + "OUT: proportional2 Proportional | gain 1 | 0 | links subtract2 \n", + "----------------------------\n", + "Level 1 Cols 3\n", + "level1col0 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", + "PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", + "COM: subtract3 Subtract | 0 | links weighted_sum4 weighted_sum3 \n", + "OUT: proportional3 Proportional | gain 1 | 0 | links subtract3 \n", + "----------------------------\n", + "level1col1 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", + "PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", + "COM: subtract4 Subtract | 0 | links weighted_sum6 weighted_sum5 \n", + "OUT: proportional4 Proportional | gain 1 | 0 | links subtract4 \n", + "----------------------------\n", + "level1col2 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", + "PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", + "COM: subtract5 Subtract | 0 | links weighted_sum8 weighted_sum7 \n", + "OUT: proportional5 Proportional | gain 1 | 0 | links subtract5 \n", + "----------------------------\n", + "Level 2 Cols 3\n", + "level2col0 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant Constant | 0 \n", + "PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", + "COM: subtract6 Subtract | 0 | links constant weighted_sum9 \n", + "OUT: proportional6 Proportional | gain 1 | 0 | links subtract6 \n", + "----------------------------\n", + "level2col1 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant1 Constant | 0 \n", + "PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", + "COM: subtract7 Subtract | 0 | links constant1 weighted_sum10 \n", + "OUT: proportional7 Proportional | gain 1 | 0 | links subtract7 \n", + "----------------------------\n", + "level2col2 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant2 Constant | 0 \n", + "PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", + "COM: subtract8 Subtract | 0 | links constant2 weighted_sum11 \n", + "OUT: proportional8 Proportional | gain 1 | 0 | links subtract8 \n", + "----------------------------\n", + "POST: postcon Constant | 10 \n", + "**************************\n" + ] + } + ], + "source": [ + "hpct.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#FunctionsList.getInstance().report() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a hierarchy from a configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'precon', 'value': 5, 'links': {}}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level0col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'weighted_sum', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level0col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum1', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable1', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'weighted_sum1', 1: 'variable1'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level0col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable2', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'weighted_sum2', 1: 'variable2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 1}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level1col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum4', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum3', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract3', 'value': 0, 'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional3', 'value': 0, 'links': {0: 'subtract3'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level1col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum6', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum5', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract4', 'value': 0, 'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional4', 'value': 0, 'links': {0: 'subtract4'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level1col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum8', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum7', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract5', 'value': 0, 'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional5', 'value': 0, 'links': {0: 'subtract5'}, 'gain': 1}}}}}}, 'level2': {'level': 2, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level2col0', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum9', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract6', 'value': 0, 'links': {0: 'constant', 1: 'weighted_sum9'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional6', 'value': 0, 'links': {0: 'subtract6'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level2col1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum10', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract7', 'value': 0, 'links': {0: 'constant1', 1: 'weighted_sum10'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional7', 'value': 0, 'links': {0: 'subtract7'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level2col2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum11', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract8', 'value': 0, 'links': {0: 'constant2', 1: 'weighted_sum11'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional8', 'value': 0, 'links': {0: 'subtract8'}, 'gain': 1}}}}}}}, 'post': {'post0': {'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}}}\n" + ] + } + ], + "source": [ + "config = hpct.get_config()\n", + "print(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# h = PCTHierarchy.from_config(config, namespace=namespace)\n", + "h = PCTHierarchy.from_config(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "assert h.get_config() == hpct.get_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing a Hierarchy\n", + "\n", + "The hierarchy details can be viewed as a summary. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**************************\n", + "pcthierarchy PCTHierarchy [3, 3, 3] a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "--------------------------\n", + "PRE: precon Constant | 5 \n", + "Level 0 Cols 3\n", + "level0col0 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", + "PER: variable Variable | 0 \n", + "COM: subtract Subtract | 0 | links weighted_sum variable \n", + "OUT: proportional Proportional | gain 10 | 0 | links subtract \n", + "----------------------------\n", + "level0col1 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", + "PER: variable1 Variable | 0 \n", + "COM: subtract1 Subtract | 0 | links weighted_sum1 variable1 \n", + "OUT: proportional1 Proportional | gain 10 | 0 | links subtract1 \n", + "----------------------------\n", + "level0col2 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \n", + "PER: variable2 Variable | 0 \n", + "COM: subtract2 Subtract | 0 | links weighted_sum2 variable2 \n", + "OUT: proportional2 Proportional | gain 10 | 0 | links subtract2 \n", + "----------------------------\n", + "Level 1 Cols 3\n", + "level1col0 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", + "PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", + "COM: subtract3 Subtract | 0 | links weighted_sum4 weighted_sum3 \n", + "OUT: proportional3 Proportional | gain 10 | 0 | links subtract3 \n", + "----------------------------\n", + "level1col1 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", + "PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", + "COM: subtract4 Subtract | 0 | links weighted_sum6 weighted_sum5 \n", + "OUT: proportional4 Proportional | gain 10 | 0 | links subtract4 \n", + "----------------------------\n", + "level1col2 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \n", + "PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \n", + "COM: subtract5 Subtract | 0 | links weighted_sum8 weighted_sum7 \n", + "OUT: proportional5 Proportional | gain 10 | 0 | links subtract5 \n", + "----------------------------\n", + "Level 2 Cols 3\n", + "level2col0 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant Constant | 1 \n", + "PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", + "COM: subtract6 Subtract | 0 | links constant weighted_sum9 \n", + "OUT: proportional6 Proportional | gain 10 | 0 | links subtract6 \n", + "----------------------------\n", + "level2col1 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant1 Constant | 1 \n", + "PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", + "COM: subtract7 Subtract | 0 | links constant1 weighted_sum10 \n", + "OUT: proportional7 Proportional | gain 10 | 0 | links subtract7 \n", + "----------------------------\n", + "level2col2 PCTNode a809a703-bec9-11ef-8809-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant2 Constant | 1 \n", + "PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \n", + "COM: subtract8 Subtract | 0 | links constant2 weighted_sum11 \n", + "OUT: proportional8 Proportional | gain 10 | 0 | links subtract8 \n", + "----------------------------\n", + "POST: postcon Constant | 10 \n", + "**************************\n" + ] + } + ], + "source": [ + "hpct.get_node(2,0).get_function('reference').set_value(1)\n", + "hpct.get_node(2,1).get_function('reference').set_value(1)\n", + "hpct.get_node(2,2).get_function('reference').set_value(1)\n", + "for level in range(3):\n", + " for col in range(3):\n", + " hpct.get_node(level,col).get_function('output').set_property('gain', 10)\n", + "hpct.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hierarchy details can be viewed as a configuration. That configuration can be used to create a hierarchy, as shown above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'PCTHierarchy',\n", + " 'name': 'pcthierarchy',\n", + " 'pre': {'pre0': {'type': 'Constant',\n", + " 'name': 'precon',\n", + " 'value': 5,\n", + " 'links': {}}},\n", + " 'levels': {'level0': {'level': 0,\n", + " 'nodes': {'col0': {'col': 0,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level0col0',\n", + " 'refcoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum',\n", + " 'value': 0,\n", + " 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'percoll': {'0': {'type': 'Variable',\n", + " 'name': 'variable',\n", + " 'value': 0,\n", + " 'links': {}}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum', 1: 'variable'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract'},\n", + " 'gain': 10}}}},\n", + " 'col1': {'col': 1,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level0col1',\n", + " 'refcoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum1',\n", + " 'value': 0,\n", + " 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'percoll': {'0': {'type': 'Variable',\n", + " 'name': 'variable1',\n", + " 'value': 0,\n", + " 'links': {}}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract1',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum1', 1: 'variable1'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional1',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract1'},\n", + " 'gain': 10}}}},\n", + " 'col2': {'col': 2,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level0col2',\n", + " 'refcoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum2',\n", + " 'value': 0,\n", + " 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'percoll': {'0': {'type': 'Variable',\n", + " 'name': 'variable2',\n", + " 'value': 0,\n", + " 'links': {}}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract2',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum2', 1: 'variable2'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional2',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract2'},\n", + " 'gain': 10}}}}}},\n", + " 'level1': {'level': 1,\n", + " 'nodes': {'col0': {'col': 0,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level1col0',\n", + " 'refcoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum4',\n", + " 'value': 0,\n", + " 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'percoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum3',\n", + " 'value': 0,\n", + " 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract3',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional3',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract3'},\n", + " 'gain': 10}}}},\n", + " 'col1': {'col': 1,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level1col1',\n", + " 'refcoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum6',\n", + " 'value': 0,\n", + " 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'percoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum5',\n", + " 'value': 0,\n", + " 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract4',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional4',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract4'},\n", + " 'gain': 10}}}},\n", + " 'col2': {'col': 2,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level1col2',\n", + " 'refcoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum8',\n", + " 'value': 0,\n", + " 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'percoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum7',\n", + " 'value': 0,\n", + " 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract5',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional5',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract5'},\n", + " 'gain': 10}}}}}},\n", + " 'level2': {'level': 2,\n", + " 'nodes': {'col0': {'col': 0,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level2col0',\n", + " 'refcoll': {'0': {'type': 'Constant',\n", + " 'name': 'constant',\n", + " 'value': 1,\n", + " 'links': {}}},\n", + " 'percoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum9',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract6',\n", + " 'value': 0,\n", + " 'links': {0: 'constant', 1: 'weighted_sum9'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional6',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract6'},\n", + " 'gain': 10}}}},\n", + " 'col1': {'col': 1,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level2col1',\n", + " 'refcoll': {'0': {'type': 'Constant',\n", + " 'name': 'constant1',\n", + " 'value': 1,\n", + " 'links': {}}},\n", + " 'percoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum10',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract7',\n", + " 'value': 0,\n", + " 'links': {0: 'constant1', 1: 'weighted_sum10'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional7',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract7'},\n", + " 'gain': 10}}}},\n", + " 'col2': {'col': 2,\n", + " 'node': {'type': 'PCTNode',\n", + " 'name': 'level2col2',\n", + " 'refcoll': {'0': {'type': 'Constant',\n", + " 'name': 'constant2',\n", + " 'value': 1,\n", + " 'links': {}}},\n", + " 'percoll': {'0': {'type': 'WeightedSum',\n", + " 'name': 'weighted_sum11',\n", + " 'value': 0,\n", + " 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n", + " 'weights': [1, 1, 1]}},\n", + " 'comcoll': {'0': {'type': 'Subtract',\n", + " 'name': 'subtract8',\n", + " 'value': 0,\n", + " 'links': {0: 'constant2', 1: 'weighted_sum11'}}},\n", + " 'outcoll': {'0': {'type': 'Proportional',\n", + " 'name': 'proportional8',\n", + " 'value': 0,\n", + " 'links': {0: 'subtract8'},\n", + " 'gain': 10}}}}}}},\n", + " 'post': {'post0': {'type': 'Constant',\n", + " 'name': 'postcon',\n", + " 'value': 10,\n", + " 'links': {}}}}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hpct.get_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the output function, which will be the output function of the last node, or the last item of the post-processor functions, if present." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}\n" + ] + } + ], + "source": [ + "link = hpct.get_output_function()\n", + "print(link.get_config())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hierarhcy can also be viewed graphically as a network of connected nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ryoung\\Versioning\\python\\nbdev\\pct\\pct\\hierarchy.py:298: UserWarning:\n", + "\n", + "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + "\n" + ] + } + ], + "source": [ + "ahpct = PCTHierarchy(2,2, links=\"dense\")\n", + "\n", + "test = 3\n", + "if test==1:\n", + " g = ahpct.graph()\n", + " pos=graphviz_layout(g, prog='dot')\n", + " nx.draw(g, pos=pos, with_labels=True, font_size=12, font_weight='bold', node_color='red', node_size=500)\n", + "\n", + "if test ==2:\n", + " g = ahpct.graph()\n", + " pos = nx.multipartite_layout(g, subset_key=\"layer\", align='horizontal')\n", + " pos['constant1'][0]+=0.2\n", + " c = pos['constant1'][0]\n", + " print(c)\n", + " nx.draw(g, pos=pos, with_labels=True, font_weight='bold', node_color='red', node_size=750, arrowsize=25)\n", + "\n", + "if test ==3:\n", + " if os.name=='nt': \n", + " ahpct.draw(file=\"ahpct.png\", node_size=1500, figsize=(10,10))# with_labels=True, font_weight='bold', node_color='red', node_size=500, arrowsize=25, align='vertical'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running a hierarchy\n", + "\n", + "The hierachy can be run once by calling itself. The verbose flag will print the computations to the screen." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.000 \n", + "level0col0 0.000 0.000 0.000 0.000 \n", + "level0col1 0.000 0.000 0.000 0.000 \n", + "level0col2 0.000 0.000 0.000 0.000 \n", + "level1col0 0.000 0.000 0.000 0.000 \n", + "level1col1 0.000 0.000 0.000 0.000 \n", + "level1col2 0.000 0.000 0.000 0.000 \n", + "level2col0 1.000 0.000 1.000 10.000 \n", + "level2col1 1.000 0.000 1.000 10.000 \n", + "level2col2 1.000 0.000 1.000 10.000 \n", + "10.000 \n" + ] + }, + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hpct(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A hierarchy can be executed with the \"run()\" method, providing the number of iterations to run. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hpct1 = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, links=\"dense\")\n", + "namespace=hpct1.namespace\n", + "hpct1.get_node(2,0).get_function('reference').set_value(1)\n", + "hpct1.get_node(2,1).get_function('reference').set_value(1)\n", + "hpct1.get_node(2,2).get_function('reference').set_value(1)\n", + "for level in range(3):\n", + " for col in range(3):\n", + " hpct1.get_node(level,col).get_function('output').set_property('gain', 10)\n", + "\n", + "hpct1.run(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing Data\n", + "\n", + "If the hierarchy is created with the \"history\" flag equal to True, the data can be retrieved for each node. The node is accessed by specifying the row and column within the hierarchy. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'refcoll': {'weighted_sum6': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'percoll': {'weighted_sum5': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 'comcoll': {'subtract4': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'outcoll': {'proportional4': [0, 300, 300, 300, 300, 300, 300, 300, 300, 300]}}\n" + ] + } + ], + "source": [ + "print(hpct1.get_node(1,1).history.data)\n", + "assert hpct1.get_node(1,1).history.data == {'refcoll': {'weighted_sum6': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'percoll': {'weighted_sum5': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'comcoll': {'subtract4': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'outcoll': {'proportional4': [0.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0]}}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save and Load" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save a hierarchy to file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hpct1.save(\"hpct.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a hierarchy from file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'json' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[26], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#loaded = PCTHierarchy.load(\"hpct.json\", clear=False, namespace=namespace)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m loaded \u001b[38;5;241m=\u001b[39m \u001b[43mPCTHierarchy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhpct.json\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclear\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m loaded\u001b[38;5;241m.\u001b[39msummary()\n", + "File \u001b[1;32m~\\Versioning\\python\\nbdev\\pct\\pct\\hierarchy.py:828\u001b[0m, in \u001b[0;36mPCTHierarchy.load\u001b[1;34m(cls, file, clear, namespace)\u001b[0m\n\u001b[0;32m 825\u001b[0m FunctionsList\u001b[38;5;241m.\u001b[39mgetInstance()\u001b[38;5;241m.\u001b[39mclear()\n\u001b[0;32m 827\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(file) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m--> 828\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[43mjson\u001b[49m\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[0;32m 829\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_config(config, namespace\u001b[38;5;241m=\u001b[39mnamespace)\n", + "\u001b[1;31mNameError\u001b[0m: name 'json' is not defined" + ] + } + ], + "source": [ + "#loaded = PCTHierarchy.load(\"hpct.json\", clear=False, namespace=namespace)\n", + "loaded = PCTHierarchy.load(\"hpct.json\", clear=False)\n", + "loaded.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# https://matplotlib.org/3.1.0/gallery/color/named_colors.html\n", + "loaded.draw(with_edge_labels=True, color_mapping={'w':'aqua','c':'limegreen','s':'goldenrod', 'p':'red', 'v':'silver'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Examples\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Build a hierarchy by adding nodes and functions manually.\n", + "\n", + "Create an empty hierarchy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**************************\n", + "pcthierarchy PCTHierarchy [] 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "--------------------------\n", + "PRE: None\n", + "POST: None\n", + "**************************\n" + ] + } + ], + "source": [ + "myhpct = PCTHierarchy()\n", + "namespace=myhpct.namespace\n", + "myhpct.summary(build=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add a node. Then nodes at particular positions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**************************\n", + "pcthierarchy PCTHierarchy [2, 1] 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "--------------------------\n", + "PRE: None\n", + "Level 0 Cols 2\n", + "pctnode2 PCTNode 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant2 Constant | 0 \n", + "PER: variable2 Variable | 0 \n", + "COM: subtract2 Subtract | 0 \n", + "OUT: proportional2 Proportional | gain 1 | 0 \n", + "----------------------------\n", + "pctnode PCTNode 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant Constant | 0 \n", + "PER: variable Variable | 0 \n", + "COM: subtract Subtract | 0 \n", + "OUT: proportional Proportional | gain 1 | 0 \n", + "----------------------------\n", + "Level 1 Cols 1\n", + "pctnode1 PCTNode 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant1 Constant | 0 \n", + "PER: variable1 Variable | 0 \n", + "COM: subtract1 Subtract | 0 \n", + "OUT: proportional1 Proportional | gain 1 | 0 \n", + "----------------------------\n", + "POST: None\n", + "**************************\n" + ] + } + ], + "source": [ + "myhpct.add_node(PCTNode(namespace=namespace))\n", + "myhpct.add_node(PCTNode(namespace=namespace), level=1)\n", + "myhpct.add_node(PCTNode(namespace=namespace), level=0)\n", + "myhpct.summary(build=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace functions at particular positions in the hierarchy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myhpct.insert_function(level=0, col=0, collection=\"perception\", function=Proportional(3, name=\"prop2\", namespace=namespace))\n", + "myhpct.insert_function(level=1, col=0, collection=\"perception\", function=WeightedSum(weights=[1,1], name=\"wsum\", namespace=namespace))\n", + "myhpct.insert_function(level=0, col=1, collection=\"reference\", function=Proportional(1, name=\"passthru\", namespace=namespace))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add pre and post processor functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myhpct.add_preprocessor(Constant(1, name=\"cons1\", namespace=namespace))\n", + "myhpct.add_preprocessor(Proportional(5, name=\"prop1\", namespace=namespace))\n", + "myhpct.add_postprocessor(Proportional(5, name=\"postprop1\", namespace=namespace))\n", + "myhpct.add_postprocessor(Proportional(5, name=\"postprop2\", namespace=namespace))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Link the functions together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myhpct.set_links(\"prop1\", \"cons1\")\n", + "myhpct.set_links(\"prop2\", \"prop1\")\n", + "myhpct.add_links(\"wsum\", \"prop2\", \"variable\")\n", + "myhpct.set_links(\"passthru\", \"proportional1\")\n", + "myhpct.set_links(\"postprop1\", \"proportional\")\n", + "myhpct.set_links(\"postprop2\", \"postprop1\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**************************\n", + "pcthierarchy PCTHierarchy [2, 1] 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "--------------------------\n", + "PRE: cons1 Constant | 1 \n", + "prop1 Proportional | gain 5 | 0 | links cons1 \n", + "Level 0 Cols 2\n", + "pctnode2 PCTNode 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant2 Constant | 1 \n", + "PER: prop2 Proportional | gain 3 | 0 | links prop1 \n", + "COM: subtract2 Subtract | 0 | links constant2 prop2 \n", + "OUT: proportional2 Proportional | gain 10 | 0 | links subtract2 \n", + "----------------------------\n", + "pctnode PCTNode 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "----------------------------\n", + "REF: passthru Proportional | gain 1 | 0 | links proportional1 \n", + "PER: variable Variable | 0 \n", + "COM: subtract Subtract | 0 | links passthru variable \n", + "OUT: proportional Proportional | gain 10 | 0 | links subtract \n", + "----------------------------\n", + "Level 1 Cols 1\n", + "pctnode1 PCTNode 14646f3b-bec9-11ef-9f39-8cf8c5b8669e\n", + "----------------------------\n", + "REF: constant1 Constant | 1 \n", + "PER: wsum WeightedSum | weights [1, 1] | 0 | links prop2 variable \n", + "COM: subtract1 Subtract | 0 | links constant1 wsum \n", + "OUT: proportional1 Proportional | gain 10 | 0 | links subtract1 \n", + "----------------------------\n", + "POST: postprop1 Proportional | gain 5 | 0 | links proportional \n", + "postprop2 Proportional | gain 5 | 0 | links postprop1 \n", + "**************************\n" + ] + } + ], + "source": [ + "myhpct.get_node(0,0).get_function('reference').set_value(1)\n", + "myhpct.get_node(1,0).get_function('reference').set_value(1)\n", + "myhpct.get_node(0,0).get_function('output').set_property('gain', 10)\n", + "myhpct.get_node(0,1).get_function('output').set_property('gain', 10)\n", + "myhpct.get_node(1,0).get_function('output').set_property('gain', 10)\n", + "myhpct.summary(build=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myhpctconfig = myhpct.get_config()\n", + "#print(myhpctconfig)\n", + "assert myhpctconfig == {'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'cons1', 'value': 1, 'links': {}}, 'pre1': {'type': 'Proportional', 'name': 'prop1', 'value': 0, 'links': {0: 'cons1'}, 'gain': 5}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'Proportional', 'name': 'prop2', 'value': 0, 'links': {0: 'prop1'}, 'gain': 3}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'constant2', 1: 'prop2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 10}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'pctnode', 'refcoll': {'0': {'type': 'Proportional', 'name': 'passthru', 'value': 0, 'links': {0: 'proportional1'}, 'gain': 1}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'passthru', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'wsum', 'value': 0, 'links': {0: 'prop2', 1: 'variable'}, 'weights': [1.0, 1.0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'constant1', 1: 'wsum'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 10}}}}}}}, 'post': {'post0': {'type': 'Proportional', 'name': 'postprop1', 'value': 0, 'links': {0: 'proportional'}, 'gain': 5}, 'post1': {'type': 'Proportional', 'name': 'postprop2', 'value': 0, 'links': {0: 'postprop1'}, 'gain': 5}}}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the order in which the node will be processed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myhpct.set_order([\"pctnode2\", \"pctnode1\", \"pctnode\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the hierarchy once." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.000 5.000 \n", + "pctnode2 1.000 15.000 -14.000 -140.000 \n", + "pctnode1 1.000 15.000 -14.000 -140.000 \n", + "pctnode -140.000 0.000 -140.000 -1400.000 \n", + "-7000.000 -35000.000 \n", + "-35000\n" + ] + } + ], + "source": [ + "out = myhpct(verbose=True)\n", + "print(out)\n", + "assert out == -35000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# config = {'type': 'Individual', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'CartPoleV1', 'name': 'CartPoleV1', 'value': [0.03498833197860944, 0.20994561633454428, 0.012668159509212712, -0.2705237130920193, 0.047656152654718356], 'links': {0: 'Action1'}, 'env_name': 'CartPole-v1', 'reward': 1.0, 'done': False, 'info': {}}, 'pre1': {'type': 'IndexedParameter', 'name': 'ICV', 'value': 0.20994561633454428, 'links': {0: 'CartPoleV1'}, 'index': 1}, 'pre2': {'type': 'IndexedParameter', 'name': 'ICP', 'value': 0.03498833197860944, 'links': {0: 'CartPoleV1'}, 'index': 0}, 'pre3': {'type': 'IndexedParameter', 'name': 'IPV', 'value': -0.2705237130920193, 'links': {0: 'CartPoleV1'}, 'index': 3}, 'pre4': {'type': 'IndexedParameter', 'name': 'IPA', 'value': 0.012668159509212712, 'links': {0: 'CartPoleV1'}, 'index': 2}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'L0C0', 'refcoll': {'0': {'type': 'EAConstant', 'name': 'RL0C0', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'EAWeightedSum', 'name': 'PL0C0', 'value': -0.2705237130920193, 'links': {0: 'ICV', 1: 'ICP', 2: 'IPV', 3: 'IPA'}, 'weights': [0, 0, 1, 0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'CL0C0', 'value': 0.2705237130920193, 'links': {0: 'RL0C0', 1: 'PL0C0'}}}, 'outcoll': {'0': {'type': 'EAProportional', 'name': 'OL0C0', 'value': -0.05046166000036782, 'links': {0: 'CL0C0'}, 'gain': -0.1865332226280776}}}}}}}, 'post': {'post0': {'type': 'EAWeightedSum', 'name': 'Action1', 'value': -0.005282911840894066, 'links': {0: 'OL0C0'}, 'weights': [0.10469159835121472]}}}\n", + "# ind = PCTHierarchy.from_config(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TotalError limit:250, limit_exceeded:False, : RootSumSquaredError error_response:2.23606797749979\n", + "[0] \n", + "level0col0 0.000 0.000 0.000 0.000 \n", + "\n", + "Current score=2.23606797749979\n", + "[1] \n", + "level0col0 0.000 0.000 0.000 0.000 \n", + "\n", + "Current score=2.23606797749979\n", + "[2] \n", + "level0col0 0.000 0.000 0.000 0.000 \n", + "\n", + "Current score=2.23606797749979\n", + "[3] \n", + "level0col0 0.000 0.000 0.000 0.000 \n", + "\n", + "Current score=2.23606797749979\n", + "[4] \n", + "level0col0 0.000 0.000 0.000 0.000 \n", + "\n", + "Current score=2.23606797749979\n", + "2.23606797749979\n" + ] + } + ], + "source": [ + "from pct.errors import RootSumSquaredError, TotalError\n", + "\n", + "er = RootSumSquaredError()\n", + "te = TotalError(error_response=er, limit=250,min=True) \n", + "te.add_error_data([1, 2])\n", + "print(te)\n", + "\n", + "\n", + "hpct = PCTHierarchy(1,1,error_collector=te)\n", + "hpct.run(steps=5, verbose=True)\n", + "\n", + "\n", + "err=te.error()\n", + "print(err)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import nbdev; nbdev.nbdev_export()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pct/_modidx.py b/pct/_modidx.py index 68f2cfe3..461bfc44 100644 --- a/pct/_modidx.py +++ b/pct/_modidx.py @@ -1044,13 +1044,7 @@ 'pct/helpers.py'), 'pct.helpers.SolutionsDataManager.reload_data': ( 'helpers.html#solutionsdatamanager.reload_data', 'pct/helpers.py')}, - 'pct.hierarchy': { 'pct.hierarchy.FunctionsData': ('hierarchy.html#functionsdata', 'pct/hierarchy.py'), - 'pct.hierarchy.FunctionsData.__init__': ('hierarchy.html#functionsdata.__init__', 'pct/hierarchy.py'), - 'pct.hierarchy.FunctionsData.add_data': ('hierarchy.html#functionsdata.add_data', 'pct/hierarchy.py'), - 'pct.hierarchy.FunctionsData.add_fitness': ('hierarchy.html#functionsdata.add_fitness', 'pct/hierarchy.py'), - 'pct.hierarchy.FunctionsData.add_list': ('hierarchy.html#functionsdata.add_list', 'pct/hierarchy.py'), - 'pct.hierarchy.FunctionsData.add_reward': ('hierarchy.html#functionsdata.add_reward', 'pct/hierarchy.py'), - 'pct.hierarchy.PCTHierarchy': ('hierarchy.html#pcthierarchy', 'pct/hierarchy.py'), + 'pct.hierarchy': { 'pct.hierarchy.PCTHierarchy': ('hierarchy.html#pcthierarchy', 'pct/hierarchy.py'), 'pct.hierarchy.PCTHierarchy.__call__': ('hierarchy.html#pcthierarchy.__call__', 'pct/hierarchy.py'), 'pct.hierarchy.PCTHierarchy.__init__': ('hierarchy.html#pcthierarchy.__init__', 'pct/hierarchy.py'), 'pct.hierarchy.PCTHierarchy.add_links': ('hierarchy.html#pcthierarchy.add_links', 'pct/hierarchy.py'), @@ -1356,6 +1350,12 @@ 'pct.putils.Counter.get': ('putils.html#counter.get', 'pct/putils.py'), 'pct.putils.Counter.get_limit': ('putils.html#counter.get_limit', 'pct/putils.py'), 'pct.putils.Counter.set_limit': ('putils.html#counter.set_limit', 'pct/putils.py'), + 'pct.putils.FunctionsData': ('putils.html#functionsdata', 'pct/putils.py'), + 'pct.putils.FunctionsData.__init__': ('putils.html#functionsdata.__init__', 'pct/putils.py'), + 'pct.putils.FunctionsData.add_data': ('putils.html#functionsdata.add_data', 'pct/putils.py'), + 'pct.putils.FunctionsData.add_fitness': ('putils.html#functionsdata.add_fitness', 'pct/putils.py'), + 'pct.putils.FunctionsData.add_list': ('putils.html#functionsdata.add_list', 'pct/putils.py'), + 'pct.putils.FunctionsData.add_reward': ('putils.html#functionsdata.add_reward', 'pct/putils.py'), 'pct.putils.FunctionsList': ('putils.html#functionslist', 'pct/putils.py'), 'pct.putils.FunctionsList.__init__': ('putils.html#functionslist.__init__', 'pct/putils.py'), 'pct.putils.FunctionsList.add_function': ('putils.html#functionslist.add_function', 'pct/putils.py'), diff --git a/pct/hierarchy.py b/pct/hierarchy.py index 841bd2c7..53912abf 100644 --- a/pct/hierarchy.py +++ b/pct/hierarchy.py @@ -1,12 +1,13 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/04_hierarchy.ipynb. # %% auto 0 -__all__ = ['FunctionsData', 'PCTHierarchy'] +__all__ = ['PCTHierarchy'] # %% ../nbs/04_hierarchy.ipynb 4 #import numpy as np from os import sep import uuid +import json # %% ../nbs/04_hierarchy.ipynb 5 @@ -16,46 +17,10 @@ from .functions import BaseFunction, HPCTFUNCTION from .environments import EnvironmentFactory from .errors import BaseErrorCollector -from .putils import floatListsToString, PCTRunProperties +from .putils import floatListsToString, PCTRunProperties, FunctionsData -# %% ../nbs/04_hierarchy.ipynb 9 -class FunctionsData(): - "Data collected for a set of functions" - def __init__(self): - self.data = {} - - def add_data(self, func): - name = func.get_name() - if name in self.data.keys(): - self.data[name].append(func.get_value()) - else: - dlist=[] - self.data[name]=dlist - self.data[name].append(func.get_value()) - - def add_reward(self, func): - name = 'reward' - if name in self.data.keys(): - self.data[name].append(func.get_reward()) - else: - dlist=[] - self.data[name]=dlist - self.data[name].append(func.get_reward()) - - def add_fitness(self, func): - name = 'fitness' - if name in self.data.keys(): - self.data[name].append(func.get_fitness()) - else: - dlist=[] - self.data[name]=dlist - self.data[name].append(func.get_fitness()) - - def add_list(self, key, list): - self.data[key]= list - -# %% ../nbs/04_hierarchy.ipynb 12 +# %% ../nbs/04_hierarchy.ipynb 8 class PCTHierarchy(): "A hierarchical perceptual control system, of PCTNodes." diff --git a/pct/putils.py b/pct/putils.py index 043f1478..5f81d9b5 100644 --- a/pct/putils.py +++ b/pct/putils.py @@ -1,7 +1,7 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/01_putils.ipynb. # %% auto 0 -__all__ = ['SingletonObjects', 'UniqueNamer', 'FunctionsList', 'Memory', 'NumberStats', 'dynamic_module_import', +__all__ = ['SingletonObjects', 'UniqueNamer', 'FunctionsList', 'Memory', 'NumberStats', 'FunctionsData', 'dynamic_module_import', 'dynamic_class_load', 'get_drive', 'loadjson', 'Counter', 'set_dirs', 'stringIntListToListOfInts', 'stringFloatListToListOfFloats', 'stringListToListOfStrings', 'listNumsToString', 'round_lists', 'floatListsToString', 'limit_large_float', 'sigmoid', 'smooth', 'sigmoid_array', 'dot', 'list_of_ones', @@ -290,17 +290,53 @@ def report(self): print(f'Min: {self.min:4.3f}') -# %% ../nbs/01_putils.ipynb 19 +# %% ../nbs/01_putils.ipynb 14 +class FunctionsData(): + "Data collected for a set of functions" + def __init__(self): + self.data = {} + + def add_data(self, func): + name = func.get_name() + if name in self.data.keys(): + self.data[name].append(func.get_value()) + else: + dlist=[] + self.data[name]=dlist + self.data[name].append(func.get_value()) + + def add_reward(self, func): + name = 'reward' + if name in self.data.keys(): + self.data[name].append(func.get_reward()) + else: + dlist=[] + self.data[name]=dlist + self.data[name].append(func.get_reward()) + + def add_fitness(self, func): + name = 'fitness' + if name in self.data.keys(): + self.data[name].append(func.get_fitness()) + else: + dlist=[] + self.data[name]=dlist + self.data[name].append(func.get_fitness()) + + def add_list(self, key, list): + self.data[key]= list + +# %% ../nbs/01_putils.ipynb 20 def dynamic_module_import(modulename, package=None): if modulename not in sys.modules: importlib.import_module(modulename, package) -# %% ../nbs/01_putils.ipynb 20 +# %% ../nbs/01_putils.ipynb 21 def dynamic_class_load(modulename, classname): module = importlib.import_module(modulename) my_class = getattr(module, classname) -# %% ../nbs/01_putils.ipynb 22 +# %% ../nbs/01_putils.ipynb 23 def get_drive(): if os.name == 'nt': drive = os.path.abspath(os.sep) @@ -308,13 +344,13 @@ def get_drive(): drive = os.path.abspath(os.sep)+'mnt'+os.sep+'c'+os.sep return drive -# %% ../nbs/01_putils.ipynb 24 +# %% ../nbs/01_putils.ipynb 25 def loadjson(file): with open(file) as f: rtn = json.load(f) return rtn -# %% ../nbs/01_putils.ipynb 27 +# %% ../nbs/01_putils.ipynb 28 class Counter(object): def __init__(self, limit=1000, init=0, step=1, print=100, pause=False, display=10): @@ -339,7 +375,7 @@ def get_limit(self): def set_limit(self, limit): self.limit=limit -# %% ../nbs/01_putils.ipynb 29 +# %% ../nbs/01_putils.ipynb 30 def set_dirs(dirs): if dirs is None: out = {} @@ -363,7 +399,7 @@ def set_dirs(dirs): -# %% ../nbs/01_putils.ipynb 30 +# %% ../nbs/01_putils.ipynb 31 def stringIntListToListOfInts(strList, delimiter): #listRes = list(strList.split(",")) #print(listRes) @@ -372,7 +408,7 @@ def stringIntListToListOfInts(strList, delimiter): result.append(int(item)) return result -# %% ../nbs/01_putils.ipynb 31 +# %% ../nbs/01_putils.ipynb 32 def stringFloatListToListOfFloats(strList, delimiter): #listRes = list(strList.split(",")) #print(listRes) @@ -381,7 +417,7 @@ def stringFloatListToListOfFloats(strList, delimiter): result.append(float(item)) return result -# %% ../nbs/01_putils.ipynb 32 +# %% ../nbs/01_putils.ipynb 33 def stringListToListOfStrings(strList, delimiter=','): #listRes = list(strList.split(",")) #print(listRes) @@ -390,14 +426,14 @@ def stringListToListOfStrings(strList, delimiter=','): result.append(item.strip()) return result -# %% ../nbs/01_putils.ipynb 33 +# %% ../nbs/01_putils.ipynb 34 def listNumsToString(list): str = "" for item in list: str += f'{item}' return str -# %% ../nbs/01_putils.ipynb 34 +# %% ../nbs/01_putils.ipynb 35 def round_lists(alist, formatted, places): if isinstance(alist, str): raise Exception(f'Value {alist} should be a number in round_lists.') @@ -413,21 +449,21 @@ def round_lists(alist, formatted, places): if rtd is not None: formatted.append(rtd) -# %% ../nbs/01_putils.ipynb 35 +# %% ../nbs/01_putils.ipynb 36 def floatListsToString(alist, places): flist = [] if len(alist)>0: round_lists(alist,flist,places) return f'{flist}' -# %% ../nbs/01_putils.ipynb 36 +# %% ../nbs/01_putils.ipynb 37 def limit_large_float(val, limit=10000000): if abs(val) > limit: val = - np.sign(val) * limit return val -# %% ../nbs/01_putils.ipynb 37 +# %% ../nbs/01_putils.ipynb 38 def sigmoid(x, range, slope) : val = 0 if abs(x) > 10000000: @@ -443,7 +479,7 @@ def sigmoid(x, range, slope) : return val -# %% ../nbs/01_putils.ipynb 38 +# %% ../nbs/01_putils.ipynb 39 def smooth(new_val, old_val, smooth_factor): if smooth_factor > 1 or smooth_factor < 0: raise Exception(f'smooth_factor {smooth_factor} should be between 0 and 1') @@ -456,25 +492,25 @@ def smooth(new_val, old_val, smooth_factor): print(f'RuntimeWarning... old_val={old_val} new_val={new_val} smooth_factor={smooth_factor}') return val -# %% ../nbs/01_putils.ipynb 39 +# %% ../nbs/01_putils.ipynb 40 def sigmoid_array(x, range, slope) : exv = -x * slope / range return -range / 2 + range / (1 + np.exp(exv)) -# %% ../nbs/01_putils.ipynb 40 +# %% ../nbs/01_putils.ipynb 41 def dot(inputs, weights): sum = 0 for i in range(len(inputs)): sum += inputs[i]*weights[i] return sum -# %% ../nbs/01_putils.ipynb 41 +# %% ../nbs/01_putils.ipynb 42 def list_of_ones(num): x = [1 for _ in range(num) ] return x -# %% ../nbs/01_putils.ipynb 43 +# %% ../nbs/01_putils.ipynb 44 def limit_to_range(num, lower, upper): if num < lower: frac, _ = math.modf(num) @@ -485,7 +521,7 @@ def limit_to_range(num, lower, upper): num = upper - frac return num -# %% ../nbs/01_putils.ipynb 45 +# %% ../nbs/01_putils.ipynb 46 def show_video(): mp4list = glob.glob('video/*.mp4') if len(mp4list) > 0: @@ -503,7 +539,7 @@ def wrap_env(env): env = Monitor(env, './video', force=True) return env -# %% ../nbs/01_putils.ipynb 49 +# %% ../nbs/01_putils.ipynb 50 from pathlib import Path import os @@ -514,19 +550,19 @@ def is_in_notebooks(): return False -# %% ../nbs/01_putils.ipynb 50 +# %% ../nbs/01_putils.ipynb 51 def printtime(msg): print(f'{datetime.now()} {os.getpid()} {msg}') return time.perf_counter() -# %% ../nbs/01_putils.ipynb 51 +# %% ../nbs/01_putils.ipynb 52 def clip_value(val, range): rtn = max(min(val, range[1]), range[0]) return rtn -# %% ../nbs/01_putils.ipynb 53 +# %% ../nbs/01_putils.ipynb 54 def get_abs_tol(key): # dic = {'evolve': 0.01, 'ARC-evolve' : 0.01, 'ARC-display': 0.01, 'ARC': 0.01} dic = { 'ARC-evolve' : 0.01, 'ARC-display': 0.1, 'ARC-change' : 0.01, 'ARC-zero': 0.01, 'ARC-gradient': 0.0001} @@ -537,7 +573,7 @@ def get_abs_tol(key): # return 0.001 -# %% ../nbs/01_putils.ipynb 55 +# %% ../nbs/01_putils.ipynb 56 def get_rel_tol(key): # dic = { 'ARC-change' : 1e-3} dic = { } @@ -547,7 +583,7 @@ def get_rel_tol(key): # return 1e-6 -# %% ../nbs/01_putils.ipynb 56 +# %% ../nbs/01_putils.ipynb 57 def map_to_int_odd_range(val=None, inrange=None, outrange=None): a = round(val) b = clip_value(a, inrange) @@ -561,7 +597,7 @@ def map_to_int_even_range(val=None, inrange=None, outrange=None): rtn = math.floor(b) + int((outrange[1] - outrange[0] + 1 )/2) + 1 return rtn -# %% ../nbs/01_putils.ipynb 59 +# %% ../nbs/01_putils.ipynb 60 class TimerError(Exception): """A custom exception used to report errors in use of Timer class""" @@ -602,7 +638,7 @@ def total(self): def count(self): return self._counter -# %% ../nbs/01_putils.ipynb 62 +# %% ../nbs/01_putils.ipynb 63 class PCTRunProperties(): @classmethod @@ -680,7 +716,7 @@ def get_file_props(self, filepath): return drive, property_dir, file -# %% ../nbs/01_putils.ipynb 64 +# %% ../nbs/01_putils.ipynb 65 def get_ram_mb(): import psutil # Get the current process ID