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hebbian_weights_update.py
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hebbian_weights_update.py
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import numpy as np
from numba import njit
@njit
def hebbian_update_A(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += heb_coeffs[idx] * o0[i] * o1[j]
heb_offset = weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += heb_coeffs[idx] * o1[i] * o2[j]
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += heb_coeffs[idx] * o2[i] * o3[j]
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_AD(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += heb_coeffs[idx][0] * o0[i] * o1[j] + heb_coeffs[idx][1]
heb_offset = weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += heb_coeffs[idx][0] * o1[i] * o2[j] + heb_coeffs[idx][1]
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += heb_coeffs[idx][0] * o2[i] * o3[j] + heb_coeffs[idx][1]
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_AD_lr(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += (heb_coeffs[idx][0] * o0[i] * o1[j] + heb_coeffs[idx][1]) * heb_coeffs[idx][2]
heb_offset = weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += (heb_coeffs[idx][0] * o1[i] * o2[j] + heb_coeffs[idx][1]) * heb_coeffs[idx][2]
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += (heb_coeffs[idx][0] * o2[i] * o3[j] + heb_coeffs[idx][1]) * heb_coeffs[idx][2]
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_ABC(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += ( heb_coeffs[idx][0] * o0[i] * o1[j]
+ heb_coeffs[idx][1] * o0[i]
+ heb_coeffs[idx][2] * o1[j])
heb_offset += weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += ( heb_coeffs[idx][0] * o1[i] * o2[j]
+ heb_coeffs[idx][1] * o1[i]
+ heb_coeffs[idx][2] * o2[j])
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += ( heb_coeffs[idx][0] * o2[i] * o3[j]
+ heb_coeffs[idx][1] * o2[i]
+ heb_coeffs[idx][2] * o3[j])
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_ABC_lr(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o0[i] * o1[j]
+ heb_coeffs[idx][1] * o0[i]
+ heb_coeffs[idx][2] * o1[j])
heb_offset += weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o1[i] * o2[j]
+ heb_coeffs[idx][1] * o1[i]
+ heb_coeffs[idx][2] * o2[j])
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o2[i] * o3[j]
+ heb_coeffs[idx][1] * o2[i]
+ heb_coeffs[idx][2] * o3[j])
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_ABCD(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += heb_coeffs[idx][3] + ( heb_coeffs[idx][0] * o0[i] * o1[j]
+ heb_coeffs[idx][1] * o0[i]
+ heb_coeffs[idx][2] * o1[j])
heb_offset += weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += heb_coeffs[idx][3] + ( heb_coeffs[idx][0] * o1[i] * o2[j]
+ heb_coeffs[idx][1] * o1[i]
+ heb_coeffs[idx][2] * o2[j])
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += heb_coeffs[idx][3] + ( heb_coeffs[idx][0] * o2[i] * o3[j]
+ heb_coeffs[idx][1] * o2[i]
+ heb_coeffs[idx][2] * o3[j])
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_ABCD_lr_D_in(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
## Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o0[i] * o1[j]
+ heb_coeffs[idx][1] * o0[i]
+ heb_coeffs[idx][2] * o1[j] + heb_coeffs[idx][4])
heb_offset += weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o1[i] * o2[j]
+ heb_coeffs[idx][1] * o1[i]
+ heb_coeffs[idx][2] * o2[j] + heb_coeffs[idx][4])
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o2[i] * o3[j]
+ heb_coeffs[idx][1] * o2[i]
+ heb_coeffs[idx][2] * o3[j] + heb_coeffs[idx][4])
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_ABCD_lr_D_out(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o0[i] * o1[j]
+ heb_coeffs[idx][1] * o0[i]
+ heb_coeffs[idx][2] * o1[j]) + heb_coeffs[idx][4]
heb_offset += weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o1[i] * o2[j]
+ heb_coeffs[idx][1] * o1[i]
+ heb_coeffs[idx][2] * o2[j]) + heb_coeffs[idx][4]
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o2[i] * o3[j]
+ heb_coeffs[idx][1] * o2[i]
+ heb_coeffs[idx][2] * o3[j]) + heb_coeffs[idx][4]
return weights1_2, weights2_3, weights3_4
@njit
def hebbian_update_ABCD_lr_D_in_and_out(heb_coeffs, weights1_2, weights2_3, weights3_4, o0, o1, o2, o3):
heb_offset = 0
# Layer 1
for i in range(weights1_2.shape[1]):
for j in range(weights1_2.shape[0]):
idx = (weights1_2.shape[0]-1)*i + i + j
weights1_2[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o0[i] * o1[j]
+ heb_coeffs[idx][1] * o0[i]
+ heb_coeffs[idx][2] * o1[j] + heb_coeffs[idx][4]) + heb_coeffs[idx][5]
heb_offset += weights1_2.shape[1] * weights1_2.shape[0]
# Layer 2
for i in range(weights2_3.shape[1]):
for j in range(weights2_3.shape[0]):
idx = heb_offset + (weights2_3.shape[0]-1)*i + i+j
weights2_3[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o1[i] * o2[j]
+ heb_coeffs[idx][1] * o1[i]
+ heb_coeffs[idx][2] * o2[j] + heb_coeffs[idx][4]) + heb_coeffs[idx][5]
heb_offset += weights2_3.shape[1] * weights2_3.shape[0]
# Layer 3
for i in range(weights3_4.shape[1]):
for j in range(weights3_4.shape[0]):
idx = heb_offset + (weights3_4.shape[0]-1)*i + i+j
weights3_4[:,i][j] += heb_coeffs[idx][3] * ( heb_coeffs[idx][0] * o2[i] * o3[j]
+ heb_coeffs[idx][1] * o2[i]
+ heb_coeffs[idx][2] * o3[j] + heb_coeffs[idx][4]) + heb_coeffs[idx][5]
return weights1_2, weights2_3, weights3_4