View ball_tracking_6.py
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ball_df.dropna(inplace=True) | |
print(ball_df) |
View ball_tracking_5.py
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from sklearn.metrics import classification_report | |
y_pred = rfc.predict(x_val) | |
print(classification_report(y_val,y_pred)) |
View ball_tracking_4.py
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from sklearn.ensemble import RandomForestClassifier | |
rfc = RandomForestClassifier(max_depth=3) | |
rfc.fit(x_tr,y_tr) |
View ball_tracking_3.py
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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) |
View ball_tracking_2.py
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img_copy = np.copy(gray) | |
cv2.drawContours(img_copy, contours, -1, (0,255,0), 3) | |
plt.imshow(img_copy, cmap='gray') |
View ball_tracking_1.py
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image, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
View bpe_11.py
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i=0 | |
while(True): | |
#compute frequency | |
pairs = get_stats(oov) | |
#extract keys | |
pairs = pairs.keys() | |
#find the pairs available in the learned operations |
View bpe_10.py
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#applying BPE to OOV | |
oov ='lowest' | |
#tokenize OOV into characters | |
oov = " ".join(list(oov)) | |
#append </w> | |
oov = oov + ' </w>' | |
#create a dictionary |
View bpe_9.py
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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 |
View bpe_8.py
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#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) |
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