def get_kernel(self):
if self._kernel_opt[0] == "rbf":
return Kernel.gaussian(self._kernel_opt[1])
elif self._kernel_opt[0] == "linear":
return Kernel.linear()
elif self._kernel_opt[0] == "poly":
return Kernel._polykernel(self._kernel_opt[1], self._kernel_opt[2])
else:
return 0
def starter(classifier):
if classifier == 1:
query_results = Database().retrieve_many(image_ids=None,
collection_type="training")
training_data = np.array([item["vector"] for item in query_results])
labels = np.array([item["label"] for item in query_results])
classifer = svm.binary_classification_qp(kernel=Kernel._polykernel(
5)) # try 5 and 10 for dimensions in polykernel
labels = np.where(labels <= 0, -1, 1)
classifer.fit(training_data, labels)
query_results = Database().retrieve_many(image_ids=None,
collection_type="testing")
testing_data = np.array([item["vector"] for item in query_results])
y = classifer.predict(testing_data)
m = len(y)
images = [item["image_id"] for item in query_results]
metadata = pandas.read_csv(Config().metadata_file())
oy = []
correct = 0
for i in range(len(images)):
image_id = images[i] + ".jpg"
temp = "".join(metadata[metadata.imageName == image_id]
["aspectOfHand"].values)
oy.append(1 if "dorsal" in temp else -1)
if oy[i] == y[i]:
correct += 1
print(images)
print(oy)
print("correctly classified : ", correct)
print("Incorrectly classified : ", (m - correct))
print("accuracy : ", (correct * 1.0) / (m * 1.0))
k_th_eigenvector_all = []
for i in range(len(images)):
arr = []
if (y[i] == 1):
val = 'dorsal'
else:
val = 'palmer'
arr.append((images[i] + ".jpg", val))
k_th_eigenvector_all.append(arr)
print(k_th_eigenvector_all)
k_th_eigenvector_all = pandas.DataFrame(k_th_eigenvector_all)
visualizer.visualize_svm_classifier(k_th_eigenvector_all, 'SVM')
elif classifier == 2:
# retrive Training data from Database
query_results = Database().retrieve_many(image_ids=None,
collection_type="training")
training_data = np.array([item["vector"] for item in query_results])
labels = np.array([item["label"] for item in query_results])
classifer = dt(3)
classifer.fit(training_data, labels)
query_results = Database().retrieve_many(image_ids=None,
collection_type="testing")
testing_data = np.array([item["vector"] for item in query_results])
y = classifer.predict(testing_data)
print(y)
m = len(y)
images = [item["image_id"] for item in query_results]
metadata = pandas.read_csv(Config().metadata_file())
oy = []
correct = 0
for i in range(len(images)):
image_id = images[i] + ".jpg"
temp = "".join(metadata[metadata.imageName == image_id]
["aspectOfHand"].values)
oy.append(1 if "dorsal" in temp else 0)
if oy[i] == y[i]:
correct += 1
print(images)
print(oy)
print("correctly classified : ", correct)
print("Incorrectly classified : ", (m - correct))
print("accuracy : ", (correct * 1.0) / (m * 1.0))
visualizer.visualize_task4(images, y, "Decision Tree")
elif classifier == 3:
# PPR
training_image_vectors = Database().retrieve_many(
image_ids=None, collection_type="training")
testing_image_vectors = Database().retrieve_many(
image_ids=None, collection_type="testing")
# accuracy_list = []
# k_list = []
# for i in range(3, 180):
page_rank = PageRank(
k=8,
labelled_images=training_image_vectors,
unlabelled_images=testing_image_vectors,
)
result = page_rank.label_images()
metadata = pandas.read_csv(Config().metadata_file())
correct, incorrect = 0, 0
imagename_list, aspect_list = [], []
for imagename, aspect in result.items():
imagename_list.append(imagename)
aspect_list.append(aspect)
if aspect in str(metadata[metadata["imageName"] == imagename +
".jpg"]["aspectOfHand"]):
correct += 1
else:
incorrect += 1
print("Correct prediction = ", correct)
print("Incorrect prediction = ", incorrect)
print("Prediction accuracy = ", float(correct) / (incorrect + correct))
