def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
z = x - self.x_opt
out = tf.linalg.norm(z, axis=1)**2 + self.f_opt
return out
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
z = self.t_osz(x - self.x_opt)
out = tf.reduce_sum(self.c * z**2, axis=1) + self.f_opt
return out
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
z = tf.transpose(self.t_osz(self.r @ tf.transpose(x - self.x_opt)))
return 1.e6 * z[:, 0]**2 + tf.reduce_sum(z[:, 1:]**2,
axis=1) + self.f_opt
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
z = tf.transpose(self.c * self.r @ tf.transpose(x) + 1 / 2)
a = z[:, :-1] ** 2 - z[:, 1:]
b = z[:, :-1] - 1
return tf.reduce_sum(100 * a ** 2 + b ** 2, axis=1) + self.f_opt
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
x = tf.transpose(x - self.x_opt)
z = tf.transpose(self.r @ self.t_asy_beta(self.r @ x))
out = z[:, 0]**2 + self.condition * tf.reduce_sum(z[:, 1:]**2,
axis=1) + self.f_opt
return out
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
a = x[:, :-1] ** 2 - x[:, 1:]
b = x[:, :-1] - 1
out = tf.reduce_sum(100 * a ** 2 + b ** 2, axis=1)
return out
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
# TODO: maybe flip data dimensions?
z = tf.transpose(self.mat_fac @ self.t_asy_beta(
tf.transpose(self.t_osz(x - self.x_opt))))
out = 10 * (self.dim - tf.reduce_sum(tf.cos(
2 * np.pi * z), axis=1)) + tflin.norm(z, axis=1)**2 + self.f_opt
return out
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
z = self.c * (x - self.x_opt) + 1
z_end = z[:, 1:]
z_begin = z[:, :-1]
a = z_begin ** 2 - z_end
b = z_begin - 1
return tf.reduce_sum(100 * a ** 2 + b ** 2, axis=1) + self.f_opt
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
y = self.t_osz(x - self.x_opt)
s = np.zeros_like(y)
for i in range(self.dim):
if i % 2 == 0:
s[:,
i] = np.where(y[:, i] > 0,
10 * np.power(np.sqrt(10), i / (self.dim - 1)),
np.power(np.sqrt(10), i / (self.dim - 1)))
z = s * y
out = 10 * (self.dim - np.sum(np.cos(2 * np.pi * z), axis=1)) + np.linalg.norm(z, axis=1) ** 2 \
+ 100 * self.f_pen(x) + self.f_opt
return out
def evaluate_full(self, x, context=None):
x = tf_atleast_2d(x)
assert x.shape[1] == self.dim
return x[:, 0]**2 + 1e6 * tf.reduce_sum(x[:, 1:]**2, axis=1)
