def main():
input_csv_file_name = sys.argv[1]
output_csv_file_name = sys.argv[2]
# input values are in the form of [age, weight, height]
input_values = Reader.csv(input_csv_file_name)
cases = [
(0.001, 100),
(0.005, 100),
(0.01, 100),
(0.05, 100),
(0.1, 100),
(0.5, 100),
(1, 100),
(5, 100),
(10, 100),
#custom
(0.01, 1000),
]
Reporter.write_output(file_name=output_csv_file_name,
content="",
should_overwrite_file=True)
for learning_rate, iterations in cases:
# reset betas each time
betas = [
# intercept 0
0,
# age
0,
# weight
0
]
training_inputs = [[x[0], x[1]] for x in input_values]
expected_classifiers = [x[2] for x in input_values]
betas = GradientDescent.run(training_inputs=training_inputs,
expected_classifiers=expected_classifiers,
learning_rate=learning_rate,
betas=betas,
iterations=iterations)
# write lines to output file
Reporter.write_output(
file_name=output_csv_file_name,
content=",".join(
map(str,
[learning_rate, iterations, betas[0], betas[1], betas[2]]))
+ "\n",
)
Visualizer.draw_chart(input_values=training_inputs,
weights=betas,
file_name="figures/figure_" +
"_".join(map(str, [learning_rate, iterations])))
def main():
input_csv_file_name = sys.argv[1]
output_csv_file_name = sys.argv[2]
# input values are in the form of [feature_1, feature_2, label]
input_values = Reader.csv(input_csv_file_name)
# Track previous weights and allow to compare against latest weight to check convergence
previous_weights = [0, 0, 0]
weights = None
Reporter.write_output(file_name=output_csv_file_name,
content="",
should_overwrite_file=True)
training_inputs = [[x[0], x[1]] for x in input_values]
results = [x[2] for x in input_values]
iterations = 0
while (previous_weights != weights):
# Past the initial condition, we want to track the previous_weight
if (weights != None):
# update previous weight so we can remember for comparison
previous_weights = weights
# import ipdb; ipdb.set_trace()
# weights will be list in the form of [b or w_0, w_1, w_2]
weights = PerceptronLearning.run(training_inputs=training_inputs,
results=results,
initial_weights=previous_weights,
iterations=1)
# write lines to output file
Reporter.write_output(
file_name=output_csv_file_name,
content=','.join(map(str, [weights[1], weights[2], weights[0]])) +
"\n",
)
# create png images of the figures
Visualizer.draw_chart(input_values=input_values,
weights=weights,
file_name="figures/figure_" + str(iterations))
iterations += 1
