# PCNN
Python implementation of the Pulse Coupled Neural Network (PCNN) alongside multiple variations:
- Classical PCNN
- Feature Linking Model (FLM)
- Intersecting Cortical Model (ICM)
- Multi Linking Model (MLM)
- Spiking Cortical Model (SCM)
- Sigmoidal Linking Model (SLM)
Install:
```
pip install pulse_coupled_nn
```
Usage example:
```
import numpy as np
import matplotlib.pyplot as plt
from pulse_coupled_nn import FLM
from pulse_coupled_nn import ICM
from pulse_coupled_nn import ClassicalPCNN
from pulse_coupled_nn import SCM
from pulse_coupled_nn import SLM
def run_image_segm(gamma=1, beta=2, v_theta=400, kernel_size=3, kernel='gaussian'):
image = np.array(
[[230, 230, 230, 230, 115, 115, 115, 115],
[230, 230, 230, 230, 115, 115, 115, 115],
[230, 230, 205, 205, 103, 103, 115, 115],
[230, 230, 205, 205, 103, 103, 115, 115],
[230, 230, 205, 205, 103, 103, 115, 115],
[230, 230, 230, 230, 115, 115, 115, 115],
[230, 230, 230, 230, 115, 115, 115, 115]]
)
model = ClassicalPCNN(image.shape, kernel, kernel_size=kernel_size)
segm_image = model.segment_image(image, gamma=gamma, beta=beta, v_theta=v_theta, kernel_type='gaussian')
plt.imshow(image)
plt.colorbar()
plt.show()
plt.imshow(segm_image)
plt.colorbar()
plt.show()
run_image_segm()
```
Raw data
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"description": "# PCNN\r\nPython implementation of the Pulse Coupled Neural Network (PCNN) alongside multiple variations:\r\n- Classical PCNN\r\n- Feature Linking Model (FLM)\r\n- Intersecting Cortical Model (ICM)\r\n- Multi Linking Model (MLM)\r\n- Spiking Cortical Model (SCM)\r\n- Sigmoidal Linking Model (SLM)\r\n\r\nInstall:\r\n```\r\npip install pulse_coupled_nn\r\n```\r\n\r\nUsage example:\r\n```\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom pulse_coupled_nn import FLM\r\nfrom pulse_coupled_nn import ICM\r\nfrom pulse_coupled_nn import ClassicalPCNN\r\nfrom pulse_coupled_nn import SCM\r\nfrom pulse_coupled_nn import SLM\r\n\r\n\r\ndef run_image_segm(gamma=1, beta=2, v_theta=400, kernel_size=3, kernel='gaussian'):\r\n\r\n image = np.array(\r\n [[230, 230, 230, 230, 115, 115, 115, 115],\r\n [230, 230, 230, 230, 115, 115, 115, 115],\r\n [230, 230, 205, 205, 103, 103, 115, 115],\r\n [230, 230, 205, 205, 103, 103, 115, 115],\r\n [230, 230, 205, 205, 103, 103, 115, 115],\r\n [230, 230, 230, 230, 115, 115, 115, 115],\r\n [230, 230, 230, 230, 115, 115, 115, 115]]\r\n )\r\n\r\n model = ClassicalPCNN(image.shape, kernel, kernel_size=kernel_size)\r\n segm_image = model.segment_image(image, gamma=gamma, beta=beta, v_theta=v_theta, kernel_type='gaussian')\r\n\r\n plt.imshow(image)\r\n plt.colorbar()\r\n plt.show()\r\n\r\n plt.imshow(segm_image)\r\n plt.colorbar()\r\n plt.show()\r\n\r\n\r\nrun_image_segm()\r\n```\r\n\r\n\r\n\r\n\r\n",
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