Classifying-fish-using-NN/delta_rule_salmon.py at main · danieljordan2/Classifying-fish-using-NN · GitHub

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# USAGE
# python3 delta_rule_salmon.py

# import the necessary packages
from modules.nn import Perceptron1
import numpy as np

# construct the Salmon (freshwater and saltater) dataset
X = np.array([[83, 510],
[86, 505],
[94, 490],
[118, 490],
[86, 480],
[98, 480],
[101, 472],
[120, 472],
[90, 470],
[100, 470],
[101, 470],
[105, 470],
[75, 450],
[83, 452],
[85, 450],
[85, 442],
[75, 440],
[93, 440],
[105, 440],
[52.5, 425],
[78, 431],
[82, 430],
[95, 431],
[105, 432],
[87, 422],
[95, 427],
[102, 428],
[114, 427],
[109, 420],
[111, 421],
[126, 422],
[95, 411],
[70, 397],
[80, 399],
[84, 399],
[87, 402],
[92, 404],
[98, 403],
[98, 402],
[104, 404],
[121, 402],
[106, 439],
[109, 398],
[112, 394],
[114, 397],
[107, 368],
[118, 382],
[126, 371],
[136, 357],
[95, 430],


[135, 440],
[129, 420],
[156, 420],
[128, 400],
[144, 403],
[152.5, 403],
[178, 408],
[129, 390],
[140, 390],
[149, 392],
[152.5, 394],
[154, 390],
[128, 382],
[134, 382],
[148, 382],
[152, 381],
[170, 395],
[120, 359],
[133, 373],
[138, 371],
[140, 373],
[148, 372],
[163, 370],
[170, 375],
[123, 352],
[140, 351],
[162.5, 369],
[90, 385],
[115, 355],
[117, 356],
[135, 356],
[145, 356],
[150, 355],
[152.5, 354],
[155, 352],
[123, 350],
[125, 343],
[126, 342],
[131, 342],
[144, 342],
[107, 340],
[116, 344],
[124, 341],
[144, 339],
[150, 340],
[112.5, 330],
[114, 323],
[122, 304],
[152, 301],
[118,381],
])

y = np.array([[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[0],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1],
[1]])


#IMPORTANT: Due to fact that the weights are randomly chosen it is necessary to run the algorithm several times to check and compare results.

#RESULTS: The boundary classification equation would be x2 = -6.81x1 + 3.58 (x2 = -(w1/w2)x1 - (b/w2))

# define our perceptron and train it

print("[INFO] training perceptron...")
p = Perceptron1(X.shape[1], alpha=0.00001)	#Same values than Samarasinghe (alpha=0.00001 y epochs=80)
p.fit(X, y, epochs=80)

# now that our perceptron is trained we can evaluate it
print("[INFO] testing perceptron...")

# now that our network is trained, loop over the data points
i = 1
for (x, target) in zip(X, y):
	# make a prediction on the data point and display the result
	# to our console
	pred = p.predict(x)
	print("[INFO] data={}, punto={}, ground-truth={}, pred={}".format(
		x, i, target[0], pred))
	i = i + 1

#Print the weight vector
W = p.weights()
print(W)