| Name | DarwinPy JSON |
| Version |
0.0.2
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| home_page | |
| Summary | A evolutionary computation module |
| upload_time | 2023-05-08 13:53:23 |
| maintainer | |
| docs_url | None |
| author | Pius Arhanbhunde |
| requires_python | |
| license | MIT License |
| keywords |
evolution
|
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# DarwinPy
## Introduction
DarwinPy is a Python-based evolutionary computation module that makes it easy to implement evolutionary methods such as genetic algorithms and evolutionary strategies with only a few lines of code.
## Installing DarwinPy
DarwinPy can be installed with the command `pip install DarwinPy`.
## Classes in DarwinPy
Genetics Classes (`DarwinPy.Genetics.Genetics`)
**Methods:**
* `setSearchSpace(search_space)`: Sets the `search_space` attribute of the DarwinPy object.
* `setChromosomeLength(chromosome_length)`: Sets the `chromosome_length` attribute of the DarwinPy object.
* `setPopulationSize(population_size)`: Sets the `population_size` attribute of the DarwinPy object.
* `setChromosomeMatrix(search_space)`: Sets the `chromosome_matrix` attribute of the DarwinPy object.
* `getChromosomeMatrix()`: Returns the `chromosome_matrix` attribute, which is a NumPy array.
* `getSearchSpace()`: Returns the `search_space` attribute, which is a tuple.
* `populate()`: Initializes the `chromosome_matrix` attribute with initial values or gene population.
* `select(fitness_vector)`: Selects mating pairs based on the fitness.
* `crossover(crossover_rate)`: Mates selected pairs and updates the `chromosome_matrix` attribute.
* `mutate(mutation_rate)`: Mutates the `chromosome_matrix` attribute of the DarwinPy object.
* `evolve(fitness_vector, mutation_rate, crossover_rate)`: Performs a complete genetic algorithm cycle, which includes selection, crossover, and mutation.
| Identifier | Type |
|------------|-----------|
| `setSearchSpace` | `None` |
| `setChromosomeLength` | `None` |
| `setPopulationSize` | `None` |
| `setChromosomeMatrix` | `None` |
| `getChromosomeMatrix` | `numpy.array` |
| `getSearchSpace` | `tuple` |
| `populate` | `None` |
| `select` | `None` |
| `crossover` | `None` |
| `mutate` | `None` |
| `evolve` | `None` |
**Attributes:**
* `chromosome_length`: The length of each chromosome in the DarwinPy object.
* `population_size`: The size of the population in the DarwinPy object.
* `search_space`: The search space for the genetic algorithm, which is a tuple with an upper and lower bound.
* `chromosome_matrix`: The array of chromosomes in the DarwinPy object.
* `pair_list`: The list of mating pairs.
* `data_type`: The data type in which the genetic algorithm is bounded.
| Identifier | Type |
|----------------------|---------------|
| `chromosome_length` | `int` |
| `population_size` | `int` |
| `search_space` | `tuple` |
| `chromosome_matrix` | `numpy.array` |
| `pair_list` | `list` |
| `data_type` | `type` |
## A Sample DarwinPy implementation
```python
import DarwinPy
import numpy as np
def hammingDist(goal, matrix):
result = []
for i in range(len(matrix)):
temp = 0.
for j in range(len(goal)):
if goal[j] == matrix[i][j]:
temp += 1.
result.append(temp)
return result
if __name__ == "__main__":
mouse_population = 5
mutation_rate = 0.5
search_space = (0,1)
goal = np.array([1,0,1,1,0,1,1, 0,1,1,0],int)
chromosome_length = len(goal)
mouse_species = DarwinPy.Genetics.Genetics(chromosome_length,
mouse_population, (0,1), int)
print(f"mouse species instantiated:\n {mouse_species}")
print("the goal:\n {}".format(goal))
mouse_species.populate()
print(f"get mouse matrix(GA):\n {mouse_species.getChromosomeMatrix()}")
fitness_vector = np.array(
hammingDist(goal,mouse_species.getChromosomeMatrix()),
float)
print(f"get fitness vector: {fitness_vector}")
is_goal = False
gen = 1
while is_goal == False:
print(f"Generation #{gen}\n")
gen += 1
mouse_species.evolve(fitness_vector,
mutation_rate, 0.5)
print(f"get mouse matrix(GA):\n {mouse_species.getChromosomeMatrix()}")
fitness_vector = np.array(
hammingDist(goal,mouse_species.getChromosomeMatrix()),
float)
print(f"get fitness vector: {fitness_vector}")
if chromosome_length in fitness_vector:
is_goal = True
```
Change Log
==========
0.0.1 (10/10/2021)
------------------
- First Release
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"description": "# DarwinPy\n\n ## Introduction\nDarwinPy is a Python-based evolutionary computation module that makes it easy to implement evolutionary methods such as genetic algorithms and evolutionary strategies with only a few lines of code.\n\n## Installing DarwinPy\nDarwinPy can be installed with the command `pip install DarwinPy`.\n\n## Classes in DarwinPy\nGenetics Classes (`DarwinPy.Genetics.Genetics`)\n\n**Methods:**\n* `setSearchSpace(search_space)`: Sets the `search_space` attribute of the DarwinPy object.\n* `setChromosomeLength(chromosome_length)`: Sets the `chromosome_length` attribute of the DarwinPy object.\n* `setPopulationSize(population_size)`: Sets the `population_size` attribute of the DarwinPy object.\n* `setChromosomeMatrix(search_space)`: Sets the `chromosome_matrix` attribute of the DarwinPy object.\n* `getChromosomeMatrix()`: Returns the `chromosome_matrix` attribute, which is a NumPy array.\n* `getSearchSpace()`: Returns the `search_space` attribute, which is a tuple.\n* `populate()`: Initializes the `chromosome_matrix` attribute with initial values or gene population.\n* `select(fitness_vector)`: Selects mating pairs based on the fitness.\n* `crossover(crossover_rate)`: Mates selected pairs and updates the `chromosome_matrix` attribute.\n* `mutate(mutation_rate)`: Mutates the `chromosome_matrix` attribute of the DarwinPy object.\n* `evolve(fitness_vector, mutation_rate, crossover_rate)`: Performs a complete genetic algorithm cycle, which includes selection, crossover, and mutation.\n\n| Identifier | Type |\n|------------|-----------|\n| `setSearchSpace` | `None` |\n| `setChromosomeLength` | `None` |\n| `setPopulationSize` | `None` |\n| `setChromosomeMatrix` | `None` |\n| `getChromosomeMatrix` | `numpy.array` |\n| `getSearchSpace` | `tuple` |\n| `populate` | `None` |\n| `select` | `None` |\n| `crossover` | `None` |\n| `mutate` | `None` |\n| `evolve` | `None` |\n\n**Attributes:**\n* `chromosome_length`: The length of each chromosome in the DarwinPy object.\n* `population_size`: The size of the population in the DarwinPy object.\n* `search_space`: The search space for the genetic algorithm, which is a tuple with an upper and lower bound.\n* `chromosome_matrix`: The array of chromosomes in the DarwinPy object.\n* `pair_list`: The list of mating pairs.\n* `data_type`: The data type in which the genetic algorithm is bounded.\n\n| Identifier | Type |\n|----------------------|---------------|\n| `chromosome_length` | `int` |\n| `population_size` | `int` |\n| `search_space` | `tuple` |\n| `chromosome_matrix` | `numpy.array` |\n| `pair_list` | `list` |\n| `data_type` | `type` |\n\n## A Sample DarwinPy implementation\n```python\n\nimport DarwinPy\nimport numpy as np\n\ndef hammingDist(goal, matrix):\n result = []\n for i in range(len(matrix)):\n temp = 0.\n for j in range(len(goal)):\n if goal[j] == matrix[i][j]:\n temp += 1.\n result.append(temp)\n return result\n\n\nif __name__ == \"__main__\":\n mouse_population = 5\n mutation_rate = 0.5\n search_space = (0,1)\n goal = np.array([1,0,1,1,0,1,1, 0,1,1,0],int)\n chromosome_length = len(goal)\n\n mouse_species = DarwinPy.Genetics.Genetics(chromosome_length,\n mouse_population, (0,1), int)\n print(f\"mouse species instantiated:\\n {mouse_species}\")\n\n print(\"the goal:\\n {}\".format(goal))\n\n mouse_species.populate()\n print(f\"get mouse matrix(GA):\\n {mouse_species.getChromosomeMatrix()}\")\n\n\n fitness_vector = np.array(\n hammingDist(goal,mouse_species.getChromosomeMatrix()),\n float)\n\n print(f\"get fitness vector: {fitness_vector}\")\n\n is_goal = False\n gen = 1\n while is_goal == False:\n print(f\"Generation #{gen}\\n\")\n gen += 1\n mouse_species.evolve(fitness_vector,\n mutation_rate, 0.5)\n print(f\"get mouse matrix(GA):\\n {mouse_species.getChromosomeMatrix()}\")\n fitness_vector = np.array(\n hammingDist(goal,mouse_species.getChromosomeMatrix()),\n float)\n\n print(f\"get fitness vector: {fitness_vector}\")\n if chromosome_length in fitness_vector:\n is_goal = True\n```\n\n\nChange Log\n==========\n\n0.0.1 (10/10/2021)\n------------------\n- First Release\n\n\n",
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