def regularized_logistic_regression_bounded(log_func, learning_rate, bound,
stop_threshold, pts, l):
curr_cost = regularized_cost(log_func, pts,
squared_coefficient_regularization, l)
print("Initial cost is: {}".format(curr_cost))
increasing_count = 0
consecutive_small_count = 0
itcount = 0
while True:
itcount += 1
gradvals = regularized_gradient_descent_value(
log_func, pts, squared_coefficient_regularization_gradient, l)
nextparams = next_params(log_func, learning_rate, gradvals)
log_func = logistic_function(nextparams)
last_cost, curr_cost = curr_cost, regularized_cost(
log_func, pts, squared_coefficient_regularization, l)
diff = last_cost - curr_cost
# print("Cost after iteration {} is : {} with difference {}".format(itcount, curr_cost, diff))
# print("Params at iteration {} are: {}".format(itcount, log_func["params"]))
if 0 < diff < bound:
consecutive_small_count += 1
elif diff < 0:
increasing_count += 1
if consecutive_small_count > stop_threshold:
break
elif increasing_count > stop_threshold:
raise OverflowError
return log_func
def logistic_regression(log_func, iterations, learning_rate, pts):
for itcount in range(iterations):
gradvals = non_regularized_gradient_descent_value(log_func, pts)
nextparams = next_params(log_func, learning_rate, gradvals)
log_func = logistic_function(tuple(nextparams))
curr_cost = non_regularized_cost(log_func, pts)
print("Cost after iteration {} is : {}".format(itcount, curr_cost))
return log_func
elif increasing_count > stop_threshold:
raise OverflowError
return log_func
pts = (((0.1, 0.8, 0.2), 0), ((0.4, 0.92, 0.35), 0), ((0.3, 0.89, 0.22), 0),
((0.2, 0.81, 0.27), 0), ((0.75, 0.4, 0.4), 1), ((0.7, 0.79, 0.4), 1),
((0.9, 0.52, 0.73), 1), ((0.6, 0.01, 0.99), 1))
stdpts_with_stats = standardize(pts)
stdpts = stdpts_with_stats["points"]
stats = stdpts_with_stats["stats"]
print("Standardized points: {} // stats {}".format(stdpts, stats))
params = (0.1, 0.1, 0.1, 0.1)
log_func = logistic_function(params)
print("Cost 1: {}".format(non_regularized_cost(log_func, stdpts)))
final_func = regularized_logistic_regression_bounded(log_func, 0.1, 0.000001,
10, stdpts, 0.01)
print(list(final_func["params"]))
print("Output values: {}".format([((x, y), final_func["func"](x))
for x, y in stdpts]))
print("Vals {} // Coeffs{} // Dot: {}".format(
extend_vals(pts[0][0]), final_func["params"],
dot(extend_vals(pts[0][0]), final_func["params"])))
final_func = logistic_regression_bounded(log_func, 0.1, 0.000001, 10, stdpts)