def get_lookup_table(stylization_model):
# TODO: Need to create function
table_r = np.zeros((256, 256), dtype=np.uint8)
table_g = np.zeros((256, 256), dtype=np.uint8)
table_b = np.zeros((256, 256), dtype=np.uint8)
for i in range(256):
for j in range(256):
xm = np.zeros(1, np.float)
xm[0] = i / 255
xm = PolynomialFeatures(degree=POLY_DEGREE,
include_bias=False).fit_transform(
xm.reshape(-1, 1))
xs = np.zeros(1, np.float)
xs[0] = j / 255
xs = PolynomialFeatures(degree=POLY_DEGREE,
include_bias=False).fit_transform(
xs.reshape(-1, 1))
tmp = np.append(xm, xs)
x = np.append(np.ones(1), tmp)
table_r[i][j] = np.clip(np.dot(stylization_model[0].coef_, x), 0,
1) * 255
table_g[i][j] = np.clip(np.dot(stylization_model[1].coef_, x), 0,
1) * 255
table_b[i][j] = np.clip(np.dot(stylization_model[2].coef_, x), 0,
1) * 255
# ======================================================================
print('done')
return table_r, table_g, table_b
def fit_trend(ser, n):
"""
Takes a series and fits an n-th order polynomial to the series.
Returns the predictions.
"""
#Generate ordinal values to represent timestamps for use in regression
# Create train_X and train_y
# train_X, train_y = np.asarray(ser.index.map(dt.datetime.toordinal)), np.asarray(ser) # xi's and yi's
train_X, train_y = np.asarray([i+1 for i in range(len(ser.index))]), np.asarray(ser)
# Fit a polynomial regression model - code given to you
train_X = PolynomialFeatures(n).fit_transform(train_X.reshape(-1, 1))
lin_reg = LinearRegression().fit(train_X, train_y.reshape(-1))
# Make predictions to create the trend curve
# YOUR CODE HERE
y_pred = np.asarray(lin_reg.predict(train_X))
trend_curve = y_pred
return trend_curve
