import numpy as np
from BsplineQuantRegpy import SplineCubicQuant
import matplotlib.pyplot as plt
[docs]
def quick_start2():
print('''##CODE:
import numpy as np
from BsplineQuantRegpy import SplineCubicQuant
import matplotlib.pyplot as plt
print("test_basic_fit")
x = np.linspace(0, 1, 30)
#y = x + 0.5*np.sin(10*np.pi*x) + 0.05*np.random.randn(30)
y = x*(1-x) + 0.05*np.random.randn(30)
x_eval = np.linspace(0, 1, 100)
knots = np.quantile(x, np.linspace(0, 1, 6))
result = SplineCubicQuant(x, y, knots, tau=0.5)
y_eval = result(x_eval)
print(" test_monotonicity")
knots = np.quantile(x, np.linspace(0, 1, 6))
result_m = SplineCubicQuant(x, y, knots, tau=0.5, monot=1)
y_eval_m = result_m(x_eval)
plt.plot(x,y,"*r")
plt.plot(x_eval,y_eval,color='grey')
plt.plot(x_eval,y_eval_m,color='black')
plt.show()
''')
print("test_basic_fit")
x = np.linspace(0, 1, 30)
#y = x + 0.5*np.sin(10*np.pi*x) + 0.05*np.random.randn(30)
y = x*(1-x) + 0.05*np.random.randn(30)
x_eval = np.linspace(0, 1, 100)
knots = np.quantile(x, np.linspace(0, 1, 6))
result = SplineCubicQuant(x, y, knots, tau=0.5)
y_eval = result(x_eval)
print(" test_monotonicity")
knots = np.quantile(x, np.linspace(0, 1, 6))
result_m = SplineCubicQuant(x, y, knots, tau=0.5, monot=1)
y_eval_m = result_m(x_eval)
plt.plot(x,y,"*r")
plt.plot(x_eval,y_eval,color='grey')
plt.plot(x_eval,y_eval_m,color='black')
plt.show()
def main():
quick_start2()
if __name__=="__main__":
main()