def run_blind_data(digits, answers, weights):
""" Brings in a pack of untested data and learned weights
Returns guesses and success ratio
"""
successes = 0
for temp in range(len(digits)):
guess = check_new(digits[temp], weights)
actual = answers[temp]
print("Computer's guess: {} Actual #: {}".format(guess, actual))
if guess == actual:
successes += 1
success_ratio = successes/len(digits)
return successes, success_ratio
def learn_nums(digits, answers):
""" Takes in a data set and the appropriate answers
Returns the appropriate weight set
"""
weight_set_of = [learn_loop(digits, answers, x) for x in range(10)]
return weight_set_of
def run_blind_data(digits, answers, weights):
""" Brings in a pack of untested data and learned weights
Returns guesses and success ratio
"""
successes = 0
for temp in range(len(digits)):
guess = check_new(digits[temp], weights)
actual = answers[temp]
print("Computer's guess: {} Actual #: {}".format(guess, actual))
if guess == actual:
successes += 1
success_ratio = successes/len(digits)
return successes, success_ratio
if __name__ == '__main__':
#digits = datasets.load_iris()
digits = numpy.loadtxt(open("train.csv","rb"),delimiter=",",skiprows=1)
to_test = numpy.loadtxt(open("test.csv","rb"),delimiter=",",skiprows=1)
weights = learn_nums(digits.label, answers)
for temp in range(len(digits)):
guess = check_new(to_test[temp], weights)
print(guess)