def test_funcs_multi(self):
pi = math.pi
# sin family
self.assertQuantity(data.sin(Quantity( (0,pi/2), (2,2))), (0,1), (2,0))
self.assertQuantity(data.sinh(Quantity((0,1), (2,2))), (0, math.sinh(1)), (2, math.cosh(1)*2))
self.assertQuantity(data.asin(Quantity((0,0.5), (2,2))), (0,math.asin(0.5)), (2,2/math.sqrt(1-0.5**2)))
self.assertQuantity(data.asinh(Quantity((0,1), (2,2))), (0,math.asinh(1)), (2,2/math.sqrt(1+1**2)))
# cos family
self.assertQuantity(data.cos(Quantity((0,pi/2), (2,2))), (1,0), (0,-2))
self.assertQuantity(data.cosh(Quantity((0,1), (2,2))), (1,math.cosh(1)), (0,math.sinh(1)*2))
self.assertQuantity(data.acos(Quantity((0,0.5), (2,2))), (math.acos(0),math.acos(0.5)), (-2,-2/math.sqrt(1-0.5**2)))
self.assertQuantity(data.acosh(Quantity((2,3), (2,2))), (math.acosh(2), math.acosh(3)), (2/math.sqrt(2**2-1),2/math.sqrt(3**2-1)))
# tan family
self.assertQuantity(data.tan(Quantity((0,1), (2,2))), (0,math.tan(1)), (2,2/math.cos(1)**2))
self.assertQuantity(data.tanh(Quantity((0,1), (2,2))), (0,math.tanh(1)), (2,2/math.cosh(1)**2))
self.assertQuantity(data.atan(Quantity((0,1), (2,2))), (0, math.atan(1)), (2,2/(1+1**2)))
self.assertQuantity(data.atan2(Quantity((0,1), (2,2)), Quantity((1,1), (0,0))), (0,math.atan(1)), (2,2/(1+1**2)))
self.assertQuantity(data.atanh(Quantity((0,0.5), (2,2))), (0,math.atanh(0.5)), (2,2/(1-0.5**2)))
#misc
self.assertQuantity(data.sqrt(Quantity((1,4), (2,2))), (1,2), (1,1/2))
self.assertQuantity(data.exp(Quantity((1,4), (2,2))), (math.e, math.e**4), (2 * math.e,2*math.e**4))
self.assertQuantity(data.log(Quantity((1,4), (2,2))), (0, math.log(4)), (2,1/2))
def main():
N = 40
noiseStdDev = 0.15
X_train, T_train, = datagen.cos(N, noiseStdDev)
fx = np.cos
rvm_star_model = RVMRS(
X_train, T_train, 'RBFKernel', alphaThresh=1e19,
convergenceThresh=10e-8,
)
rvm_star_model.fit()
plt.plot_regressor(
rvm_star_model, 0, 20, train_data=(X_train, T_train), true_func=fx,
save_name='rvm_star_cos.png'
)
print('rel. vecs rvm*:', rvm_star_model.keptBasisFuncs.shape)
def test_funcs(self):
pi = math.pi
# sin family
self.assertQuantity(data.sin(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.sin(Quantity(pi/2, 2)), 1, 0)
self.assertAlmostEqual(data.sin( pi/2 ), 1)
self.assertQuantity(data.sinh(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.sinh(Quantity(1, 2)), math.sinh(1), math.cosh(1)*2)
self.assertAlmostEqual( data.sinh( 1 ), math.sinh(1))
self.assertQuantity(data.asin(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.asin(Quantity(0.5, 2)), math.asin(0.5), 2/math.sqrt(1-0.5**2))
self.assertAlmostEqual( data.asin( 0.5 ), math.asin(0.5))
self.assertQuantity(data.asinh(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.asinh(Quantity(1, 2)), math.asinh(1), 2/math.sqrt(1+1**2))
self.assertAlmostEqual( data.asinh( 1 ), math.asinh(1))
# cos family
self.assertQuantity(data.cos(Quantity(0, 2)), 1, 0)
self.assertQuantity(data.cos(Quantity(pi/2, 2)), 0, -2)
self.assertAlmostEqual( data.cos( pi/2 ), 0)
self.assertQuantity(data.cosh(Quantity(0, 2)), 1, 0)
self.assertQuantity(data.cosh(Quantity(1, 2)), math.cosh(1), math.sinh(1)*2)
self.assertAlmostEqual( data.cosh( 1 ), math.cosh(1))
self.assertQuantity(data.acos(Quantity(0, 2)), math.acos(0), -2)
self.assertQuantity(data.acos(Quantity(0.5, 2)), math.acos(0.5), -2/math.sqrt(1-0.5**2))
self.assertAlmostEqual( data.acos( 0.5 ), math.acos(0.5))
self.assertQuantity(data.acosh(Quantity(2, 2)), math.acosh(2), 2/math.sqrt(2**2-1))
self.assertQuantity(data.acosh(Quantity(3, 2)), math.acosh(3), 2/math.sqrt(3**2-1))
self.assertAlmostEqual( data.acosh( 3 ), math.acosh(3))
# tan family
self.assertQuantity(data.tan(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.tan(Quantity(1, 2)), math.tan(1), 2/math.cos(1)**2)
self.assertAlmostEqual( data.tan( 1 ), math.tan(1))
self.assertQuantity(data.tanh(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.tanh(Quantity(1, 2)), math.tanh(1), 2/math.cosh(1)**2)
self.assertAlmostEqual( data.tanh( 1 ), math.tanh(1))
self.assertQuantity(data.atan(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.atan(Quantity(1, 2)), math.atan(1), 2/(1+1**2))
self.assertAlmostEqual( data.atan( 1 ), math.atan(1))
self.assertQuantity(data.atan2(Quantity(0, 2), Quantity(1, 0)), 0, 2)
self.assertQuantity(data.atan2(Quantity(1, 2), Quantity(1, 0)), math.atan(1), 2/(1+1**2))
self.assertAlmostEqual( data.atan2( 1, 1 ), math.atan(1))
self.assertQuantity(data.atan2(Quantity(2, 2), Quantity(-1, 1)), math.atan2(2, -1), math.sqrt(8) / (1+2**2))
self.assertQuantity(data.atanh(Quantity(0, 2)), 0, 2)
self.assertQuantity(data.atanh(Quantity(0.5, 2)), math.atanh(0.5), 2/(1-0.5**2))
self.assertAlmostEqual( data.atanh( 0.5 ), math.atanh(0.5))
#misc
self.assertQuantity(data.sqrt(Quantity(1, 2)), 1, 1)
self.assertQuantity(data.sqrt(Quantity(4, 2)), 2, 1/2)
self.assertAlmostEqual(data.sqrt( 4 ), 2)
self.assertQuantity(data.sqrt(Quantity(1, 2, 'm^2')), 1, 1, self.METER)
self.assertQuantity(data.exp(Quantity(1, 2)), math.e, 2 * math.e)
self.assertQuantity(data.exp(Quantity(4, 2)), math.e**4, 2*math.e**4)
self.assertAlmostEqual(data.exp( 4 ), math.e**4)
self.assertQuantity(data.log(Quantity(1, 2)), 0, 2)
self.assertQuantity(data.log(Quantity(4, 2)), math.log(4), 1/2)
self.assertAlmostEqual(data.log( 4 ), math.log(4))
self.assertQuantity(data.log2(Quantity(1, 2)), 0, 2/math.log(2))
self.assertQuantity(data.log2(Quantity(4, 2)), 2, 2/(math.log(2) * 4))
self.assertAlmostEqual(data.log2( 4 ), 2)
self.assertQuantity(data.log10(Quantity(1, 2)), 0, 2/math.log(10))
self.assertQuantity(data.log10(Quantity(100, 2)), 2, 2/(math.log(10) * 100))
self.assertAlmostEqual(data.log10( 100 ), 2)
from sys import argv
from argparse import ArgumentParser
from sklearn import svm
from sklearn.model_selection import cross_validate as cval
from numpy import linalg as la
from csv import DictWriter
import numpy as np
from kernels import get_kernel
import data
import rvm
REGRESSION_DATASETS = {
'cos': lambda: data.cos(30, 0.1),
'airfoil': data.airfoil,
'friedman2': lambda: data.friedman_2(300, 0.1),
'friedman3': lambda: data.friedman_2(300, 0.1),
'boston': lambda: data.boston_housing(506),
'slump': data.slump,
'sinc': lambda: data.sinc(100, 0.1)
}
CLASSIFICATION_DATASETS = {
'linear': lambda: data.createSimpleClassData(100, np.array([1, 1])),
'breast-cancer': lambda: data.breast_cancer(569),
'banana': data.banana,
'titanic': data.titanic,
'waveform': data.waveform,
'german': data.german,
