def __init__(self, val, der=1):
"""
Returns a Vector variable with user defined value and derivative
INPUTS
=======
val: list of floats, compulsory
Value of the Vector variable
der: float, optional, default value is 1
Derivative of the Vector variable/function of a variable
RETURNS
========
Vector class instance
NOTES
=====
PRE:
- val and der have numeric type and val must be a list
- two or fewer inputs
POST:
returns a Vector class instance with value = val and derivative = der
"""
self._val = np.array(val)
self._jacobian = der * np.eye(len(val))
scalars = [None] * len(val)
for i in range(len(val)):
scalars[i] = Scalar(val[i])
# Initialize the jacobians
for var in scalars:
var.init_jacobian(scalars)
self._scalars = scalars
def arccosh(x):
"""
Returns a constant, Scalar, or Vector object that is the arccosh of the user specified value.
INPUTS
=======
val: real valued numeric type
RETURNS
=======
Scalar or Vector class instance
NOTES
======
If the input value is a constant, each operator method returns a constant with the operation
applied. If the input value is a Scalar object, the operator method applies the operator to the value
and propagates the derivative through the chain rule, wrapping the results in a new Scalar object. If the
input value is a vector, the operator method updates the value of the element, and the jacobian of the vector,
returning a new vector object with these properties.
"""
try:
j = x._jacobian
except AttributeError:
return np.arccosh(x) # If x is a constant
else:
try:
k = j.keys() # To tell whether x is a scalar or vector
except AttributeError:
new = Vector(np.arccosh(x._val),
x._jacobian) # If x is a vector variable
try:
dict_self = x._dict.copy(
) # If x is a complex vector variable, it will update the original dictionary
for key in dict_self.keys():
dict_self[key] = dict_self[key] * -np.arccosh(
x._val) * np.tanh(x._val)
new._dict = dict_self
return new
except AttributeError:
derivative = Counter()
derivative[x] = x._jacobian * -np.arccosh(
x._val) * np.tanh(x._val)
new._dict = derivative # If x is not a complex vector variable, it will add an attribute to the new variable
return new
else:
jacobian = {
k: x.partial(k) * -np.arccosh(x._val) * np.tanh(x._val)
for k in x._jacobian.keys()
}
return Scalar(np.arccosh(x._val), jacobian)
def create_scalar(vals):
"""
Return Scalar object(s) with user defined value(s).
INPUTS
=======
vals: a list of numeric types or a single numeric type value
RETURNS
=======
scalar: if vals is a single numeric type, scalar, a Scalar instance
with user defined value, is returned as a single object
scalars: if vals is a list of numeric types, scalars, a list of Scalar
objects with values corresponding to vals, is returned as a
list
NOTES
======
This method initializes all Scalar objects desired by the user with
user defined value. Before returning these Scalar objects, this method
solidifies the variable 'universe' by seeding the jacobians of each
of the Scalar objects with appropriate values with respect to all
Scalars requested by the user.
"""
try:
scalars = [None] * len(vals)
for i in range(len(vals)):
scalars[i] = Scalar(vals[i])
# Initialize the jacobians
for var in scalars:
var.init_jacobian(scalars)
return scalars
except TypeError:
scalar = Scalar(vals)
scalar.init_jacobian([scalar])
return scalar
from Dotua.nodes.scalar import Scalar
import numpy as np
'''
Initialize local variables for testing. Since these tests need to be
independent of AutoDiff, we will simulate the initalization process by calling
init_jacobian once the entire 'universe' of variables has been defined
'''
# Define scalar objects
vars = x, y = Scalar(1), Scalar(2)
a, b = x.eval(), y.eval()
for var in vars:
var.init_jacobian(vars)
# Define functions of the scalar objects
f_1 = x + y
f_2 = y + x
f_3 = x - y
f_4 = y - x
f_5 = x * y
f_6 = y * x
f_7 = x / y
f_8 = y / x
# Slightly more complicated functions
g_1 = 10 * x + y / 2 + 1000
g_2 = -2 * x * x - 1 / y
# Exponential functions
h_1 = x**2
h_2 = 2**x
h_3 = x**y
