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Turn in toolbox. #2

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8c82cea
Make machine learn
Oct 30, 2017
03bb7fd
Finish toolbox
Oct 31, 2017
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22 changes: 17 additions & 5 deletions learning_curve.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,8 +28,8 @@ def train_model():
used to train it.
"""
data = load_digits()
num_trials = 10
train_percentages = range(5, 95, 5)
num_trials = 100
train_percentages = range(1, 99, 1)
test_accuracies = numpy.zeros(len(train_percentages))

# train models with training percentages between 5 and 90 (see
Expand All @@ -38,17 +38,29 @@ def train_model():
# variability.
# For consistency with the previous example use
# model = LogisticRegression(C=10**-10) for your learner

for i in range(0,len(train_percentages)):
scores = []
for q in range(0,num_trials):
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target,
train_size=train_percentages[i]/100)
model = LogisticRegression(C=10**-10)
model.fit(X_train, y_train)
scores.append(model.score(X_train, y_train))

# TODO: your code here
test_accuracies[i] = sum(scores)/float(len(scores))
# print("Train accuracy %f" %model.score(X_train, y_train))
# print("Test accuracy %f"%model.score(X_test, y_test))

fig = plt.figure()
plt.plot(train_percentages, test_accuracies)
plt.title('Digit Learning with '+str(num_trials)+' Trials')
plt.xlabel('Percentage of Data Used for Training')
plt.ylabel('Accuracy on Test Set')
plt.show()


if __name__ == "__main__":
# Feel free to comment/uncomment as needed
display_digits()
# train_model()
# display_digits()
train_model()