Aravind Pai aravindpai

## ball_tracking_6.py
ball_df.dropna(inplace=True)
print(ball_df)

## ball_tracking_5.py
from sklearn.metrics import classification_report
y_pred = rfc.predict(x_val)
print(classification_report(y_val,y_pred))

## ball_tracking_4.py
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(max_depth=3)
rfc.fit(x_tr,y_tr)

## ball_tracking_3.py
from sklearn.model_selection import train_test_split
x_tr,x_val,y_tr,y_val = train_test_split(features,labels, test_size=0.2, stratify=labels,random_state=0)

## ball_tracking_2.py
img_copy = np.copy(gray)
cv2.drawContours(img_copy, contours, -1, (0,255,0), 3)
plt.imshow(img_copy, cmap='gray')

## ball_tracking_1.py
image, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

## bpe_11.py
i=0
while(True):

    #compute frequency
    pairs = get_stats(oov)

    #extract keys
    pairs = pairs.keys()

    #find the pairs available in the learned operations

## bpe_10.py
#applying BPE to OOV
oov ='lowest'

#tokenize OOV into characters
oov = " ".join(list(oov))

#append </w>
oov = oov + ' </w>'

#create a dictionary

## bpe_9.py
num_merges = 10
for i in range(num_merges):

    #compute frequency of bigrams in a corpus
    pairs = get_stats(corpus)

    #compute the best pair
    best = max(pairs, key=pairs.get)

    #merge the frequent pair in corpus

## bpe_8.py
#merge the frequent pair in corpus
corpus = merge_vocab(best, corpus)
print("After Merging:", corpus)

#convert a tuple to a string
best = "".join(list(best))

#append to merge list and vocabulary
merges = []
merges.append(best)
	from sklearn.metrics import classification_report
	y_pred = rfc.predict(x_val)
	print(classification_report(y_val,y_pred))
	from sklearn.ensemble import RandomForestClassifier
	rfc = RandomForestClassifier(max_depth=3)
	rfc.fit(x_tr,y_tr)
	from sklearn.model_selection import train_test_split
	x_tr,x_val,y_tr,y_val = train_test_split(features,labels, test_size=0.2, stratify=labels,random_state=0)
	img_copy = np.copy(gray)
	cv2.drawContours(img_copy, contours, -1, (0,255,0), 3)
	plt.imshow(img_copy, cmap='gray')
	i=0
	while(True):

	#compute frequency
	pairs = get_stats(oov)

	#extract keys
	pairs = pairs.keys()

	#find the pairs available in the learned operations
	#applying BPE to OOV
	oov ='lowest'

	#tokenize OOV into characters
	oov = " ".join(list(oov))

	#append </w>
	oov = oov + ' </w>'

	#create a dictionary
	num_merges = 10
	for i in range(num_merges):

	#compute frequency of bigrams in a corpus
	pairs = get_stats(corpus)

	#compute the best pair
	best = max(pairs, key=pairs.get)

	#merge the frequent pair in corpus
	#merge the frequent pair in corpus
	corpus = merge_vocab(best, corpus)
	print("After Merging:", corpus)

	#convert a tuple to a string
	best = "".join(list(best))

	#append to merge list and vocabulary
	merges = []
	merges.append(best)