X[a][b] = 0
medians = []
for x in range(0, 16):
acceptable = []
for z in range(0, 434):
if (X[z][x] == 1) or (X[z][x] == 0):
acceptable.append(X[z][x])
med = np.median(acceptable)
medians.append(int(med))
for c in range(0, 434):
for d in range(0, 16):
if (X[c][d] != 1) and (X[c][d] != 0):
X[c][d] = medians[d]
X = X.astype(float)
X = normalize(X)
return X, y
x, y = original_clean()
model = Isomap(n_components=size, n_neighbors=45) # 45 = 48%
out = model.fit_transform(x)
out = out[:, 0:2]
plt.scatter(out[:, 0], out[:, 1], c=y, marker='o')
plt.show()
model = DBSCAN()
predicted = model.fit_predict(out)
score = v_measure_score(predicted, y)
print(score)
