def assign_pooling(data):
image_name, feature_matrix = data[0]
clusterCenters = data[1]
feature_matrix = np.array(feature_matrix)
model = KMeansModel(clusterCenters)
bow = np.zeros(len(clusterCenters))
for x in feature_matrix:
k = model.predict(x)
dist = distance.euclidean(clusterCenters[k], x)
bow[k] = max(bow[k], dist)
clusters = bow.tolist()
group = clusters.index(min(clusters)) + 1
return [image_name, group]
def assign_pooling(row, clusterCenters, pooling):
image_name = row['fileName']
feature_matrix = np.array(row['features'])
clusterCenters = clusterCenters.value
model = KMeansModel(clusterCenters)
bow = np.zeros(len(clusterCenters))
for x in feature_matrix:
k = model.predict(x)
dist = distance.euclidean(clusterCenters[k], x)
if pooling == "max":
bow[k] = max(bow[k], dist)
elif pooling == "sum":
bow[k] = bow[k] + dist
clusters = bow.tolist()
group = clusters.index(min(clusters)) + 1
#print(image_name + " in group: " + str(group))
return [(image_name, group)]
def assign_pooling(data):
row = data[0]
clusterCenters = data[1]
pooling = data[2]
image_name = row['fileName']
feature_matrix = np.array(row['features'])
model = KMeansModel(clusterCenters)
bow = np.zeros(len(clusterCenters))
for x in feature_matrix:
k = model.predict(x)
dist = distance.euclidean(clusterCenters[k], x)
if pooling == "max":
bow[k] = max(bow[k], dist)
elif pooling == "sum":
bow[k] = bow[k] + dist
clusters = bow.tolist()
group = clusters.index(min(clusters)) + 1
return [image_name, group]
ref = []
minIndex, minValue = min(enumerate(totalDist), key=operator.itemgetter(1))
ref = perm[minIndex]
# dataPoint = []
correct = 0
incorrect = 0
with open('/home/ronald/data.csv', 'r') as f:
csvReader = csv.DictReader(f)
for row in csvReader:
data = []
for i in row:
if i != 'target':
data.append(row[i])
if ref[model.predict(Vectors.dense(data))] == int(row['target']):
correct += 1
else:
# print(str(ref[model.predict(Vectors.dense(data))])+' '+str(row['target']))
incorrect += 1
# dataPoint.append(data)
print(str(correct / (incorrect + correct) * 100) + '%')
centers = []
with open('/home/ronald/kmeansModel', 'r') as f:
line = f.readline()
while line:
centers.append(line)
line = f.readline()
