def shifted_coordinates_within_range(coordinates, minimum, maximum, precision = 4):
# Handle input errors
allow_none_matrix(coordinates, 'first')
compare_scalars(minimum, maximum, 'second', 'third')
positive_integer(precision)
# Create list to return
result = []
# Handle general case
if coordinates[0] is not None:
# Generate inputs
input_points = shifted_points_within_range(coordinates, minimum, maximum, precision)
# Generate outputs
output_points = []
for point in input_points:
output_points.append(coordinates[0][1])
# Unite inputs and outputs into single list
result = unite_vectors(input_points, output_points)
# Handle no points
else:
result = coordinates
# Return result
return result
def logistic_roots_initial_value(first_constant,
second_constant,
third_constant,
initial_value,
precision=4):
# Handle input errors
four_scalars(first_constant, second_constant, third_constant,
initial_value)
positive_integer(precision)
# Create list to return
result = []
# Create pivot variable
log_argument = first_constant / initial_value - 1
# Circumvent logarithm of zero
if log_argument == 0:
log_argument = 10**(-precision)
# Create intermediary variables
numerator = log(abs(log_argument))
denominator = second_constant
ratio = numerator / denominator
# Determine root given an initial value
root = third_constant - ratio
# Round root
rounded_root = rounded_value(root, precision)
# Return result
result.append(rounded_root)
return result
def exponential_roots_initial_value(first_constant, second_constant, initial_value, precision = 4):
# Handle input errors
three_scalars(first_constant, second_constant, initial_value)
positive_integer(precision)
# Create list to return
result = []
# Create intermediary variables
numerator = log(abs(initial_value / first_constant))
denominator = log(abs(second_constant))
# Circumvent division by zero
if denominator == 0:
denominator = 10**(-precision)
# Determine root given an initial value
ratio = numerator / denominator
# Round root
rounded_ratio = rounded_value(ratio, precision)
# Return result
result.append(rounded_ratio)
return result
def sinusoidal_roots_derivative_initial_value(first_constant, second_constant, third_constant, fourth_constant, initial_value, precision = 4):
# Handle input errors
five_scalars(first_constant, second_constant, third_constant, fourth_constant, initial_value)
positive_integer(precision)
# Create intermediary list and list to return
roots = []
result = []
# Identify key ratio
ratio = initial_value / (first_constant * second_constant)
# Handle no roots
if ratio > 1 or ratio < -1:
result.append(None)
# Handle multiple roots
else:
# Handle case in which initial value is zero
if ratio == 0:
roots = sinusoidal_roots_first_derivative(first_constant, second_constant, third_constant, fourth_constant, precision)
# Handle general case
else:
radians = acos(ratio)
periodic_radians = radians / second_constant
periodic_unit = 2 * pi / second_constant
initial = third_constant + periodic_radians
roots = generate_elements(initial, periodic_unit, precision)
# Handle roots that bounce on the x-axis
if ratio == 1 or ratio == -1:
pass
# Handle roots that cross the x-axis
else:
alternative_initial = third_constant + periodic_unit - periodic_radians
generated_elements = generate_elements(alternative_initial, periodic_unit, precision)
roots.extend(generated_elements)
# Separate numerical roots, string roots, and None results
separated_roots = separate_elements(roots)
numerical_roots = separated_roots['numerical']
other_roots = separated_roots['other']
# Sort numerical roots
sorted_roots = sorted_list(numerical_roots)
# Round numerical roots
rounded_roots = rounded_list(sorted_roots, precision)
# Sort other_roots
sorted_other_roots = sorted_strings(other_roots)
# Combine numerical and non-numerical roots
result.extend(rounded_roots + sorted_other_roots)
# Return result
return result
def sinusoidal_roots_initial_value(first_constant, second_constant, third_constant, fourth_constant, initial_value, precision = 4):
# Handle input errors
five_scalars(first_constant, second_constant, third_constant, fourth_constant, initial_value)
positive_integer(precision)
# Determine roots given an initial value
result = sinusoidal_roots(first_constant, second_constant, third_constant, fourth_constant - initial_value, precision)
return result
def shifted_points_within_range(points, minimum, maximum, precision = 4):
# Handle input errors
allow_vector_matrix(points, 'first')
compare_scalars(minimum, maximum, 'second', 'third')
positive_integer(precision)
# Grab general points
general_points = []
for point in points:
# Handle coordinate pairs
if isinstance(point, list):
if isinstance(point[0], str):
general_points.append(point[0])
# Handle single coordinates
else:
if isinstance(point, str):
general_points.append(point)
# Generate options for inputs
optional_points = []
for point in general_points:
# Grab initial value and periodic unit
initial_value_index = point.find(' + ')
initial_value = float(point[:initial_value_index])
periodic_unit_index = initial_value_index + 3
periodic_unit = float(point[periodic_unit_index:-1])
# Increase or decrease initial value to fit into range
alternative_initial_value = shift_into_range(initial_value, periodic_unit, minimum, maximum)
# Generate additional values within range
generated_elements = generate_elements(alternative_initial_value, periodic_unit, precision)
optional_points += generated_elements
# Separate numerical inputs from string inputs
separated_points = separate_elements(optional_points)
numerical_points = separated_points['numerical']
other_points = separated_points['other']
# Sort numerical inputs
sorted_points = sorted_list(numerical_points)
# Reduce numerical inputs to within a given range
selected_points = [x for x in sorted_points if x >= minimum and x <= maximum]
# Round numerical inputs
rounded_points = rounded_list(selected_points, precision)
# Sort string inputs
sorted_other_points = sorted_strings(other_points)
# Combine numerical and string inputs
result = rounded_points + sorted_other_points
return result
def logarithmic_roots_derivative_initial_value(first_constant,
second_constant,
initial_value,
precision=4):
# Handle input errors
three_scalars(first_constant, second_constant, initial_value)
positive_integer(precision)
# Determine roots of derivative given an initial value
result = hyperbolic_roots(first_constant, -1 * initial_value, precision)
return result
def linear_roots_initial_value(first_constant,
second_constant,
initial_value,
precision=4):
# Handle input errors
three_scalars(first_constant, second_constant, initial_value)
positive_integer(precision)
# Determine roots given an initial value
result = linear_roots(first_constant, second_constant - initial_value,
precision)
return result
def exponential_roots_derivative_initial_value(first_constant, second_constant, initial_value, precision = 4):
# Handle input errors
three_scalars(first_constant, second_constant, initial_value)
positive_integer(precision)
# Circumvent division by zero
denominator = log(abs(second_constant))
if denominator == 0:
denominator = 10**(-precision)
# Determine root of derivative given an initial value
result = exponential_roots_initial_value(first_constant * denominator, second_constant, initial_value, precision)
return result
def quadratic_roots_derivative_initial_value(first_constant,
second_constant,
third_constant,
initial_value,
precision=4):
# Handle input errors
four_scalars(first_constant, second_constant, third_constant,
initial_value)
positive_integer(precision)
# Determine roots of derivative given an initial value
result = linear_roots(2 * first_constant, second_constant - initial_value,
precision)
return result
def exponential_roots_second_derivative(first_constant, second_constant, precision = 4):
# Handle input errors
two_scalars(first_constant, second_constant)
positive_integer(precision)
# Create list to return
result = []
# Determine root of second derivative
root = None
# Return result
result.append(root)
return result
def quadratic_roots_first_derivative(first_constant,
second_constant,
third_constant,
precision=4):
# Handle input errors
three_scalars(first_constant, second_constant, third_constant)
positive_integer(precision)
# Generate coefficients of first derivative
constants = quadratic_derivatives(first_constant, second_constant,
third_constant)['first']['constants']
# Determine roots of first derivative
result = linear_roots(*constants, precision)
return result
def rounded_list(numbers, precision=4):
# Handle input errors
allow_none_vector(numbers, 'first')
positive_integer(precision)
# Create list to return
results = []
# Iterate over input
for number in numbers:
# Store rounded values of input in list to return
results.append(rounded_value(number, precision))
# Return results
return results
def hyperbolic_roots_first_derivative(first_constant,
second_constant,
precision=4):
# Handle input errors
two_scalars(first_constant, second_constant)
positive_integer(precision)
# Create list to return
result = []
# Determine root of first derivative
root = 0.0
# Return result
result.append(root)
return result
def quadratic_roots_second_derivative(first_constant,
second_constant,
third_constant,
precision=4):
# Handle input errors
three_scalars(first_constant, second_constant, third_constant)
positive_integer(precision)
# Create list to return
result = []
# Determine root
root = None
# Return result
result.append(root)
return result
def logistic_roots_second_derivative(first_constant,
second_constant,
third_constant,
precision=4):
# Handle input errors
three_scalars(first_constant, second_constant, third_constant)
positive_integer(precision)
# Create list to return
result = []
# Determine root of second derivative
root = rounded_value(third_constant)
# Return root
result.append(root)
return result
def cubic_roots_second_derivative(first_constant,
second_constant,
third_constant,
fourth_constant,
precision=4):
# Handle input errors
four_scalars(first_constant, second_constant, third_constant,
fourth_constant)
positive_integer(precision)
# Generate coefficients of second derivative
constants = cubic_derivatives(first_constant, second_constant,
third_constant,
fourth_constant)['second']['constants']
# Determine roots of second derivative
result = linear_roots(*constants, precision)
return result
def sinusoidal_roots_second_derivative(first_constant, second_constant, third_constant, fourth_constant, precision = 4):
# Handle input errors
four_scalars(first_constant, second_constant, third_constant, fourth_constant)
positive_integer(precision)
# Generate coefficients of second derivative
constants = sinusoidal_derivatives(first_constant, second_constant, third_constant, fourth_constant)['second']['constants']
# Create intermediary variables
periodic_unit = pi / constants[1]
initial_value = constants[2]
# Generate roots based on criteria
generated_roots = generate_elements(initial_value, periodic_unit, precision)
# Return result
result = generated_roots
return result
def linear_roots_derivative_initial_value(first_constant,
second_constant,
initial_value,
precision=4):
# Handle input errors
three_scalars(first_constant, second_constant, initial_value)
positive_integer(precision)
# Create list to return
result = []
# Handle general case
if initial_value == first_constant:
result.append('All')
# Handle exception
else:
result.append(None)
# Return result
return result
def logistic_roots_derivative_initial_value(first_constant,
second_constant,
third_constant,
initial_value,
precision=4):
# Handle input errors
four_scalars(first_constant, second_constant, third_constant,
initial_value)
positive_integer(precision)
# Create intermediary list and list to return
roots = []
result = []
# Determine quadratic roots of derivative given an initial value
intermediary_roots = quadratic_roots(
initial_value, 2 * initial_value - first_constant * second_constant,
initial_value, precision)
# Handle no roots
if intermediary_roots[0] == None:
roots.append(None)
# Convert quadratic roots using logarithms
else:
for intermediary in intermediary_roots:
if intermediary == 0:
intermediary = 10**(-precision)
root = third_constant - log(abs(intermediary)) / second_constant
rounded_root = rounded_value(root, precision)
roots.append(rounded_root)
# Sort roots
sorted_roots = sorted_list(roots)
# Return result
result.extend(sorted_roots)
return result
def hyperbolic_roots_derivative_initial_value(first_constant,
second_constant,
initial_value,
precision=4):
# Handle input errors
three_scalars(first_constant, second_constant, initial_value)
positive_integer(precision)
# Create list to return
result = []
# Create intermediary variable
ratio = -1 * first_constant / initial_value
# Handle no roots
if ratio < 0:
result.append(None)
# Determine roots of derivative given an initial value
else:
radical = ratio**(1 / 2)
rounded_radical = rounded_value(radical, precision)
result.append(rounded_radical)
return result
def extrema_points(equation_type, coefficients, precision=4):
"""
Calculates the extrema of a specific function
Parameters
----------
equation_type : str
Name of the type of function for which extrema must be determined (e.g., 'linear', 'quadratic')
coefficients : list of int or float
Coefficients to use to generate the equation to investigate
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the results
Raises
------
ValueError
First argument must be either 'linear', 'quadratic', 'cubic', 'hyperbolic', 'exponential', 'logarithmic', 'logistic', or 'sinusoidal'
TypeError
Second argument must be a 1-dimensional list containing elements that are integers or floats
ValueError
Last argument must be a positive integer
Returns
-------
points['maxima'] : list of float or str
Values of the x-coordinates at which the original function has a relative maximum; if the function is sinusoidal, then only two or three results within a two-period interval will be listed, but a general form will also be included; if the function has no maxima, then it will return a list of `None`
points['minima'] : list of float or str
Values of the x-coordinates at which the original function has a relative minimum; if the function is sinusoidal, then only two or three results within a two-period interval will be listed, but a general form will also be included; if the function has no minima, then it will return a list of `None`
See Also
--------
- Roots for key functions: :func:`~regressions.analyses.roots.linear.linear_roots`, :func:`~regressions.analyses.roots.quadratic.quadratic_roots`, :func:`~regressions.analyses.roots.cubic.cubic_roots`, :func:`~regressions.analyses.roots.hyperbolic.hyperbolic_roots`, :func:`~regressions.analyses.roots.exponential.exponential_roots`, :func:`~regressions.analyses.roots.logarithmic.logarithmic_roots`, :func:`~regressions.analyses.roots.logistic.logistic_roots`, :func:`~regressions.analyses.roots.sinusoidal.sinusoidal_roots`
- Graphical analysis: :func:`~regressions.analyses.criticals.critical_points`, :func:`~regressions.analyses.intervals.sign_chart`, :func:`~regressions.analyses.maxima.maxima_points`, :func:`~regressions.analyses.minima.minima_points`, :func:`~regressions.analyses.points.key_coordinates`
Notes
-----
- Critical points for the derivative of a function: :math:`c_i = \\{ c_1, c_2, c_3, \\cdots, c_{n-1}, c_n \\}`
- X-coordinates of the extrema of the function: :math:`x_{ext} = \\{ x \\mid x \\in c_i, \\left( f'(\\frac{c_{j-1} + c_j}{2}) < 0 \\cap f'(\\frac{c_j + c_{j+1}}{2}) > 0 \\right) \\\\ \\cup \\left( f'(\\frac{c_{j-1} + c_j}{2}) > 0 \\cap f'(\\frac{c_j + c_{j+1}}{2}) < 0 \\right) \\}`
- |extrema_values|
Examples
--------
Import `extrema_points` function from `regressions` library
>>> from regressions.analyses.extrema import extrema_points
Calulate the extrema of a cubic function with coefficients 1, -15, 63, and -7
>>> points_cubic = extrema_points('cubic', [1, -15, 63, -7])
>>> print(points_cubic['maxima'])
[3.0]
>>> print(points_cubic['minima'])
[7.0]
Calulate the extrema of a sinusoidal function with coefficients 2, 3, 5, and 7
>>> points_sinusoidal = extrema_points('sinusoidal', [2, 3, 5, 7])
>>> print(points_sinusoidal['maxima'])
[5.5236, 7.618, 9.7124, '5.5236 + 2.0944k']
>>> print(points_sinusoidal['minima'])
[6.5708, 8.6652, '6.5708 + 2.0944k']
"""
# Handle input errors
select_equations(equation_type)
vector_of_scalars(coefficients, 'second')
positive_integer(precision)
# Determine maxima and minima
max_points = maxima_points(equation_type, coefficients, precision)
min_points = minima_points(equation_type, coefficients, precision)
# Create dictionary to return
result = {}
# Handle sinusoidal case
if equation_type == 'sinusoidal':
# Recreate sign chart
intervals_set = sign_chart('sinusoidal', coefficients, 1, precision)
# Grab general form
general_form = intervals_set[-1]
# Extract periodic unit
periodic_unit_index = general_form.find(' + ') + 3
periodic_unit = 2 * float(general_form[periodic_unit_index:-1])
rounded_periodic_unit = rounded_value(periodic_unit, precision)
# Create general forms for max and min
max_general_form = str(
max_points[0]) + ' + ' + str(rounded_periodic_unit) + 'k'
min_general_form = str(
min_points[0]) + ' + ' + str(rounded_periodic_unit) + 'k'
# Append general form as final element of each list
max_extended = max_points + [max_general_form]
min_extended = min_points + [min_general_form]
result = {'maxima': max_extended, 'minima': min_extended}
# Handle all other cases
else:
result = {'maxima': max_points, 'minima': min_points}
return result
def logistic_model(data, precision=4):
"""
Generates a logistic regression model from a given data set
Parameters
----------
data : list of lists of int or float
List of lists of numbers representing a collection of coordinate pairs; it must include at least 10 pairs
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the results
Raises
------
TypeError
First argument must be a 2-dimensional list
TypeError
Elements nested within first argument must be integers or floats
ValueError
First argument must contain at least 10 elements
ValueError
Last argument must be a positive integer
Returns
-------
model['constants'] : list of float
Coefficients of the resultant logistic model; the first element is the carrying capacity, the second element is the growth rate, and the third element is the sigmoid's midpoint
model['evaluations']['equation'] : func
Function that evaluates the equation of the logistic model at a given numeric input (e.g., model['evaluations']['equation'](10) would evaluate the equation of the logistic model when the independent variable is 10)
model['evaluations']['derivative'] : func
Function that evaluates the first derivative of the logistic model at a given numeric input (e.g., model['evaluations']['derivative'](10) would evaluate the first derivative of the logistic model when the independent variable is 10)
model['evaluations']['integral'] : func
Function that evaluates the integral of the logistic model at a given numeric input (e.g., model['evaluations']['integral'](10) would evaluate the integral of the logistic model when the independent variable is 10)
model['points']['roots'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the x-intercepts of the logistic model (will always be `None`)
model['points']['maxima'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the maxima of the logistic model (will always be `None`)
model['points']['minima'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the minima of the logistic model (will always be `None`)
model['points']['inflections'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the inflection points of the logistic model (will contain exactly one point)
model['accumulations']['range'] : float
Total area under the curve represented by the logistic model between the smallest independent coordinate originally provided and the largest independent coordinate originally provided (i.e., over the range)
model['accumulations']['iqr'] : float
Total area under the curve represented by the logistic model between the first and third quartiles of all the independent coordinates originally provided (i.e., over the interquartile range)
model['averages']['range']['average_value_derivative'] : float
Average rate of change of the curve represented by the logistic model between the smallest independent coordinate originally provided and the largest independent coordinate originally provided
model['averages']['range']['mean_values_derivative'] : list of float
All points between the smallest independent coordinate originally provided and the largest independent coordinate originally provided where their instantaneous rate of change equals the function's average rate of change over that interval
model['averages']['range']['average_value_integral'] : float
Average value of the curve represented by the logistic model between the smallest independent coordinate originally provided and the largest independent coordinate originally provided
model['averages']['range']['mean_values_integral'] : list of float
All points between the smallest independent coordinate originally provided and the largest independent coordinate originally provided where their value equals the function's average value over that interval
model['averages']['iqr']['average_value_derivative'] : float
Average rate of change of the curve represented by the logistic model between the first and third quartiles of all the independent coordinates originally provided
model['averages']['iqr']['mean_values_derivative'] : list of float
All points between the first and third quartiles of all the independent coordinates originally provided where their instantaneous rate of change equals the function's average rate of change over that interval
model['averages']['iqr']['average_value_integral'] : float
Average value of the curve represented by the logistic model between the first and third quartiles of all the independent coordinates originally provided
model['averages']['iqr']['mean_values_integral'] : list of float
All points between the first and third quartiles of all the independent coordinates originally provided where their value equals the function's average value over that interval
model['correlation'] : float
Correlation coefficient indicating how well the model fits the original data set (values range between 0.0, implying no fit, and 1.0, implying a perfect fit)
See Also
--------
:func:`~regressions.analyses.equations.logistic.logistic_equation`, :func:`~regressions.analyses.derivatives.logistic.logistic_derivatives`, :func:`~regressions.analyses.integrals.logistic.logistic_integral`, :func:`~regressions.analyses.roots.logistic.logistic_roots`, :func:`~regressions.statistics.correlation.correlation_coefficient`, :func:`~regressions.execute.run_all`
Notes
-----
- Provided ordered pairs for the data set: :math:`p_i = \\{ (p_{1,x}, p_{1,y}), (p_{2,x}, p_{2,y}), \\cdots, (p_{n,x}, p_{n,y}) \\}`
- Provided values for the independent variable: :math:`X_i = \\{ p_{1,x}, p_{2,x}, \\cdots, p_{n,x} \\}`
- Provided values for the dependent variable: :math:`Y_i = \\{ p_{1,y}, p_{2,y}, \\cdots, p_{n,y} \\}`
- Minimum value of the provided values for the independent variable: :math:`X_{min} \\leq p_{j,x}, \\forall p_{j,x} \\in X_i`
- Maximum value of the provided values for the independent variable: :math:`X_{max} \\geq p_{j,x}, \\forall p_{j,x} \\in X_i`
- First quartile of the provided values for the independent variable: :math:`X_{Q1}`
- Third quartile of the provided values for the independent variable: :math:`X_{Q3}`
- Mean of all provided values for the dependent variable: :math:`\\bar{y} = \\frac{1}{n}\\cdot{\\sum\\limits_{i=1}^n Y_i}`
- Resultant values for the coefficients of the logistic model: :math:`C_i = \\{ a, b, c \\}`
- Standard form for the equation of the logistic model: :math:`f(x) = \\frac{a}{1 + \\text{e}^{-b\\cdot(x - c)}}`
- First derivative of the logistic model: :math:`f'(x) = \\frac{ab\\cdot{\\text{e}^{-b\\cdot(x - c)}}}{(1 + \\text{e}^{-b\\cdot(x - c)})^2}`
- Second derivative of the logistic model: :math:`f''(x) = \\frac{2ab^2\\cdot{\\text{e}^{-2b\\cdot(x - c)}}}{(1 + \\text{e}^{-b\\cdot(x - c)})^3} - \\frac{ab^2\\cdot{\\text{e}^{-b\\cdot(x - c)}}}{(1 + \\text{e}^{-b\\cdot(x - c)})^2}`
- Integral of the logistic model: :math:`F(x) = \\frac{a}{b}\\cdot{\\ln|\\text{e}^{b\\cdot(x - c)} + 1|}`
- Potential x-values of the roots of the logistic model: :math:`x_{intercepts} = \\{ \\varnothing \\}`
- Potential x-values of the maxima of the logistic model: :math:`x_{maxima} = \\{ \\varnothing \\}`
- Potential x-values of the minima of the logistic model: :math:`x_{minima} = \\{ \\varnothing \\}`
- Potential x-values of the inflection points of the logistic model: :math:`x_{inflections} = \\{ c \\}`
- Accumulatation of the logistic model over its range: :math:`A_{range} = \\int_{X_{min}}^{X_{max}} f(x) \\,dx`
- Accumulatation of the logistic model over its interquartile range: :math:`A_{iqr} = \\int_{X_{Q1}}^{X_{Q3}} f(x) \\,dx`
- Average rate of change of the logistic model over its range: :math:`m_{range} = \\frac{f(X_{max}) - f(X_{min})}{X_{max} - X_{min}}`
- Potential x-values at which the logistic model's instantaneous rate of change equals its average rate of change over its range: :math:`x_{m,range} = \\{ c + \\frac{1}{b}\\cdot{\\ln(2m_{range})} - \\frac{1}{b}\\cdot{\\ln\\left(ab - 2m_{range} - \\sqrt{(2m_{range} - ab)^2 - 4m_{range}^2}\\right)}, \\\\ c + \\frac{1}{b}\\cdot{\\ln(2m_{range})} - \\frac{1}{b}\\cdot{\\ln\\left(ab - 2m_{range} + \\sqrt{(2m_{range} - ab)^2 - 4m_{range}^2}\\right)} \\}`
- Average value of the logistic model over its range: :math:`v_{range} = \\frac{1}{X_{max} - X_{min}}\\cdot{A_{range}}`
- Potential x-values at which the logistic model's value equals its average value over its range: :math:`x_{v,range} = \\{ c - \\frac{1}{b}\\cdot{\\ln(\\frac{a}{v_{range}} - 1)} \\}`
- Average rate of change of the logistic model over its interquartile range: :math:`m_{iqr} = \\frac{f(X_{Q3}) - f(X_{Q1})}{X_{Q3} - X_{Q1}}`
- Potential x-values at which the logistic model's instantaneous rate of change equals its average rate of change over its interquartile range: :math:`x_{m,iqr} = \\{ c + \\frac{1}{b}\\cdot{\\ln(2m_{iqr})} - \\frac{1}{b}\\cdot{\\ln\\left(ab - 2m_{iqr} - \\sqrt{(2m_{iqr} - ab)^2 - 4m_{iqr}^2}\\right)}, \\\\ c + \\frac{1}{b}\\cdot{\\ln(2m_{iqr})} - \\frac{1}{b}\\cdot{\\ln\\left(ab - 2m_{iqr} + \\sqrt{(2m_{iqr} - ab)^2 - 4m_{iqr}^2}\\right)} \\}`
- Average value of the logistic model over its interquartile range: :math:`v_{iqr} = \\frac{1}{X_{Q3} - X_{Q1}}\\cdot{A_{iqr}}`
- Potential x-values at which the logistic model's value equals its average value over its interquartile range: :math:`x_{v,iqr} = \\{ c - \\frac{1}{b}\\cdot{\\ln(\\frac{a}{v_{iqr}} - 1)} \\}`
- Predicted values based on the logistic model: :math:`\\hat{y}_i = \\{ \\hat{y}_1, \\hat{y}_2, \\cdots, \\hat{y}_n \\}`
- Residuals of the dependent variable: :math:`e_i = \\{ p_{1,y} - \\hat{y}_1, p_{2,y} - \\hat{y}_2, \\cdots, p_{n,y} - \\hat{y}_n \\}`
- Deviations of the dependent variable: :math:`d_i = \\{ p_{1,y} - \\bar{y}, p_{2,y} - \\bar{y}, \\cdots, p_{n,y} - \\bar{y} \\}`
- Sum of squares of residuals: :math:`SS_{res} = \\sum\\limits_{i=1}^n e_i^2`
- Sum of squares of deviations: :math:`SS_{dev} = \\sum\\limits_{i=1}^n d_i^2`
- Correlation coefficient for the logistic model: :math:`r = \\sqrt{1 - \\frac{SS_{res}}{SS_{dev}}}`
- |regression_analysis|
Examples
--------
Import `logistic_model` function from `regressions` library
>>> from regressions.models.logistic import logistic_model
Generate a logistic regression model for the data set [[1, 0.0000122], [2, 0.000247], [3, 0.004945], [4, 0.094852], [5, 1.0], [6, 1.905148], [7, 1.995055], [8, 1.999753], [9, 1.999988], [10, 1.999999]], then print its coefficients, roots, total accumulation over its interquartile range, and correlation
>>> model_perfect = logistic_model([[1, 0.0000122], [2, 0.000247], [3, 0.004945], [4, 0.094852], [5, 1.0], [6, 1.905148], [7, 1.995055], [8, 1.999753], [9, 1.999988], [10, 1.999999]])
>>> print(model_perfect['constants'])
[2.0, 3.0, 5.0]
>>> print(model_perfect['points']['roots'])
[None]
>>> print(model_perfect['accumulations']['iqr'])
5.9987
>>> print(model_perfect['correlation'])
1.0
Generate a logistic regression model for the data set [[1, 32], [2, 25], [3, 14], [4, 23], [5, 39], [6, 45], [7, 42], [8, 49], [9, 36], [10, 33]], then print its coefficients, inflections, total accumulation over its range, and correlation
>>> model_agnostic = logistic_model([[1, 32], [2, 25], [3, 14], [4, 23], [5, 39], [6, 45], [7, 42], [8, 49], [9, 36], [10, 33]])
>>> print(model_agnostic['constants'])
[43.9838, 0.3076, 0.9747]
>>> print(model_agnostic['points']['inflections'])
[[0.9747, 21.9919]]
>>> print(model_agnostic['accumulations']['range'])
305.9347
>>> print(model_agnostic['correlation'])
0.5875
"""
# Handle input errors
matrix_of_scalars(data, 'first')
long_vector(data)
positive_integer(precision)
# Store independent and dependent variable values separately
independent_variable = single_dimension(data, 1)
dependent_variable = single_dimension(data, 2)
# Determine key values for bounds
halved_data = half_dimension(data, 1)
dependent_lower = single_dimension(halved_data['lower'], 2)
dependent_upper = single_dimension(halved_data['upper'], 2)
mean_lower = mean_value(dependent_lower)
mean_upper = mean_value(dependent_upper)
dependent_max = max(dependent_variable)
dependent_min = min(dependent_variable)
dependent_range = dependent_max - dependent_min
independent_max = max(independent_variable)
independent_min = min(independent_variable)
independent_range = independent_max - independent_min
independent_avg = (independent_max + independent_min) / 2
# Circumvent error with bounds
if dependent_range == 0:
dependent_range = 1
if independent_range == 0:
independent_range = 1
# Create function to guide model generation
def logistic_fit(variable, first_constant, second_constant,
third_constant):
evaluation = first_constant / (1 + exp(-1 * second_constant *
(variable - third_constant)))
return evaluation
# Create list to store coefficients of generated equation
solution = []
# Handle normal case where values appear to increase in the set
if mean_upper >= mean_lower:
# Generate model
parameters, covariance = curve_fit(
logistic_fit,
independent_variable,
dependent_variable,
bounds=[(dependent_max - dependent_range, 0,
independent_avg - independent_range),
(dependent_max + dependent_range, inf,
independent_avg + independent_range)])
solution = list(parameters)
# Handle case where values do not appear to increase in the set
else:
# Generate model with inverted negative infinity and zero values
parameters, covariance = curve_fit(
logistic_fit,
independent_variable,
dependent_variable,
bounds=[(dependent_max - dependent_range, -inf,
independent_avg - independent_range),
(dependent_max + dependent_range, 0,
independent_avg + independent_range)])
solution = list(parameters)
# Eliminate zeroes from solution
coefficients = no_zeroes(solution, precision)
# Generate evaluations for function, derivative, and integral
equation = logistic_equation(*coefficients, precision)
derivative = logistic_derivatives(*coefficients,
precision)['first']['evaluation']
integral = logistic_integral(*coefficients, precision)['evaluation']
# Determine key points of graph
points = key_coordinates('logistic', coefficients, precision)
# Generate values for lower and upper bounds
five_numbers = five_number_summary(independent_variable, precision)
min_value = five_numbers['minimum']
max_value = five_numbers['maximum']
q1 = five_numbers['q1']
q3 = five_numbers['q3']
# Calculate accumulations
accumulated_range = accumulated_area('logistic', coefficients, min_value,
max_value, precision)
accumulated_iqr = accumulated_area('logistic', coefficients, q1, q3,
precision)
# Determine average values and their points
averages_range = average_values('logistic', coefficients, min_value,
max_value, precision)
averages_iqr = average_values('logistic', coefficients, q1, q3, precision)
# Create list of predicted outputs
predicted = []
for element in independent_variable:
predicted.append(equation(element))
# Calculate correlation coefficient for model
accuracy = correlation_coefficient(dependent_variable, predicted,
precision)
# Package preceding results in multiple dictionaries
evaluations = {
'equation': equation,
'derivative': derivative,
'integral': integral
}
points = {
'roots': points['roots'],
'maxima': points['maxima'],
'minima': points['minima'],
'inflections': points['inflections']
}
accumulations = {'range': accumulated_range, 'iqr': accumulated_iqr}
averages = {'range': averages_range, 'iqr': averages_iqr}
# Package all dictionaries in single dictionary to return
result = {
'constants': coefficients,
'evaluations': evaluations,
'points': points,
'accumulations': accumulations,
'averages': averages,
'correlation': accuracy
}
return result
def linear_model(data, precision = 4):
"""
Generates a linear regression model from a given data set
Parameters
----------
data : list of lists of int or float
List of lists of numbers representing a collection of coordinate pairs; it must include at least 10 pairs
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the results
Raises
------
TypeError
First argument must be a 2-dimensional list
TypeError
Elements nested within first argument must be integers or floats
ValueError
First argument must contain at least 10 elements
ValueError
Last argument must be a positive integer
Returns
-------
model['constants'] : list of float
Coefficients of the resultant linear model; the first element is the coefficient of the linear term, and the second element is the coefficient of the constant term
model['evaluations']['equation'] : func
Function that evaluates the equation of the linear model at a given numeric input (e.g., model['evaluations']['equation'](10) would evaluate the equation of the linear model when the independent variable is 10)
model['evaluations']['derivative'] : func
Function that evaluates the first derivative of the linear model at a given numeric input (e.g., model['evaluations']['derivative'](10) would evaluate the first derivative of the linear model when the independent variable is 10)
model['evaluations']['integral'] : func
Function that evaluates the integral of the linear model at a given numeric input (e.g., model['evaluations']['integral'](10) would evaluate the integral of the linear model when the independent variable is 10)
model['points']['roots'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the x-intercepts of the linear model (will contain exactly one point)
model['points']['maxima'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the maxima of the linear model (will always be `None`)
model['points']['minima'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the minima of the linear model (will always be `None`)
model['points']['inflections'] : list of lists of float
List of lists of numbers representing the coordinate pairs of all the inflection points of the linear model (will always be `None`)
model['accumulations']['range'] : float
Total area under the curve represented by the linear model between the smallest independent coordinate originally provided and the largest independent coordinate originally provided (i.e., over the range)
model['accumulations']['iqr'] : float
Total area under the curve represented by the linear model between the first and third quartiles of all the independent coordinates originally provided (i.e., over the interquartile range)
model['averages']['range']['average_value_derivative'] : float
Average rate of change of the curve represented by the linear model between the smallest independent coordinate originally provided and the largest independent coordinate originally provided
model['averages']['range']['mean_values_derivative'] : list of float
All points between the smallest independent coordinate originally provided and the largest independent coordinate originally provided where their instantaneous rate of change equals the function's average rate of change over that interval
model['averages']['range']['average_value_integral'] : float
Average value of the curve represented by the linear model between the smallest independent coordinate originally provided and the largest independent coordinate originally provided
model['averages']['range']['mean_values_integral'] : list of float
All points between the smallest independent coordinate originally provided and the largest independent coordinate originally provided where their value equals the function's average value over that interval
model['averages']['iqr']['average_value_derivative'] : float
Average rate of change of the curve represented by the linear model between the first and third quartiles of all the independent coordinates originally provided
model['averages']['iqr']['mean_values_derivative'] : list of float
All points between the first and third quartiles of all the independent coordinates originally provided where their instantaneous rate of change equals the function's average rate of change over that interval
model['averages']['iqr']['average_value_integral'] : float
Average value of the curve represented by the linear model between the first and third quartiles of all the independent coordinates originally provided
model['averages']['iqr']['mean_values_integral'] : list of float
All points between the first and third quartiles of all the independent coordinates originally provided where their value equals the function's average value over that interval
model['correlation'] : float
Correlation coefficient indicating how well the model fits the original data set (values range between 0.0, implying no fit, and 1.0, implying a perfect fit)
See Also
--------
:func:`~regressions.analyses.equations.linear.linear_equation`, :func:`~regressions.analyses.derivatives.linear.linear_derivatives`, :func:`~regressions.analyses.integrals.linear.linear_integral`, :func:`~regressions.analyses.roots.linear.linear_roots`, :func:`~regressions.statistics.correlation.correlation_coefficient`, :func:`~regressions.execute.run_all`
Notes
-----
- Provided ordered pairs for the data set: :math:`p_i = \\{ (p_{1,x}, p_{1,y}), (p_{2,x}, p_{2,y}), \\cdots, (p_{n,x}, p_{n,y}) \\}`
- Provided values for the independent variable: :math:`X_i = \\{ p_{1,x}, p_{2,x}, \\cdots, p_{n,x} \\}`
- Provided values for the dependent variable: :math:`Y_i = \\{ p_{1,y}, p_{2,y}, \\cdots, p_{n,y} \\}`
- Minimum value of the provided values for the independent variable: :math:`X_{min} \\leq p_{j,x}, \\forall p_{j,x} \\in X_i`
- Maximum value of the provided values for the independent variable: :math:`X_{max} \\geq p_{j,x}, \\forall p_{j,x} \\in X_i`
- First quartile of the provided values for the independent variable: :math:`X_{Q1}`
- Third quartile of the provided values for the independent variable: :math:`X_{Q3}`
- Mean of all provided values for the dependent variable: :math:`\\bar{y} = \\frac{1}{n}\\cdot{\\sum\\limits_{i=1}^n Y_i}`
- Resultant values for the coefficients of the linear model: :math:`C_i = \\{ a, b \\}`
- Standard form for the equation of the linear model: :math:`f(x) = a\\cdot{x} + b`
- First derivative of the linear model: :math:`f'(x) = a`
- Second derivative of the linear model: :math:`f''(x) = 0`
- Integral of the linear model: :math:`F(x) = \\frac{a}{2}\\cdot{x^2} + b\\cdot{x}`
- Potential x-values of the roots of the linear model: :math:`x_{intercepts} = \\{ -\\frac{b}{a} \\}`
- Potential x-values of the maxima of the linear model: :math:`x_{maxima} = \\{ \\varnothing \\}`
- Potential x-values of the minima of the linear model: :math:`x_{minima} = \\{ \\varnothing \\}`
- Potential x-values of the inflection points of the linear model: :math:`x_{inflections} = \\{ \\varnothing \\}`
- Accumulatation of the linear model over its range: :math:`A_{range} = \\int_{X_{min}}^{X_{max}} f(x) \\,dx`
- Accumulatation of the linear model over its interquartile range: :math:`A_{iqr} = \\int_{X_{Q1}}^{X_{Q3}} f(x) \\,dx`
- Average rate of change of the linear model over its range: :math:`m_{range} = \\frac{f(X_{max}) - f(X_{min})}{X_{max} - X_{min}}`
- Potential x-values at which the linear model's instantaneous rate of change equals its average rate of change over its range: :math:`x_{m,range} = \\{ [X_{min}, X_{max}] \\}`
- Average value of the linear model over its range: :math:`v_{range} = \\frac{1}{X_{max} - X_{min}}\\cdot{A_{range}}`
- Potential x-values at which the linear model's value equals its average value over its range: :math:`x_{v,range} = \\{ -\\frac{b - v_{range}}{a} \\}`
- Average rate of change of the linear model over its interquartile range: :math:`m_{iqr} = \\frac{f(X_{Q3}) - f(X_{Q1})}{X_{Q3} - X_{Q1}}`
- Potential x-values at which the linear model's instantaneous rate of change equals its average rate of change over its interquartile range: :math:`x_{m,iqr} = \\{ [X_{Q1}, X_{Q3}] \\}`
- Average value of the linear model over its interquartile range: :math:`v_{iqr} = \\frac{1}{X_{Q3} - X_{Q1}}\\cdot{A_{iqr}}`
- Potential x-values at which the linear model's value equals its average value over its interquartile range: :math:`x_{v,iqr} = \\{ -\\frac{b - v_{iqr}}{a} \\}`
- Predicted values based on the linear model: :math:`\\hat{y}_i = \\{ \\hat{y}_1, \\hat{y}_2, \\cdots, \\hat{y}_n \\}`
- Residuals of the dependent variable: :math:`e_i = \\{ p_{1,y} - \\hat{y}_1, p_{2,y} - \\hat{y}_2, \\cdots, p_{n,y} - \\hat{y}_n \\}`
- Deviations of the dependent variable: :math:`d_i = \\{ p_{1,y} - \\bar{y}, p_{2,y} - \\bar{y}, \\cdots, p_{n,y} - \\bar{y} \\}`
- Sum of squares of residuals: :math:`SS_{res} = \\sum\\limits_{i=1}^n e_i^2`
- Sum of squares of deviations: :math:`SS_{dev} = \\sum\\limits_{i=1}^n d_i^2`
- Correlation coefficient for the linear model: :math:`r = \\sqrt{1 - \\frac{SS_{res}}{SS_{dev}}}`
- |regression_analysis|
Examples
--------
Import `linear_model` function from `regressions` library
>>> from regressions.models.linear import linear_model
Generate a linear regression model for the data set [[1, 30], [2, 27], [3, 24], [4, 21], [5, 18], [6, 15], [7, 12], [8, 9], [9, 6], [10, 3]], then print its coefficients, roots, total accumulation over its interquartile range, and correlation
>>> model_perfect = linear_model([[1, 30], [2, 27], [3, 24], [4, 21], [5, 18], [6, 15], [7, 12], [8, 9], [9, 6], [10, 3]])
>>> print(model_perfect['constants'])
[-3.0, 33.0]
>>> print(model_perfect['points']['roots'])
[[11.0, 0.0]]
>>> print(model_perfect['accumulations']['iqr'])
82.5
>>> print(model_perfect['correlation'])
1.0
Generate a linear regression model for the data set [[1, 32], [2, 25], [3, 14], [4, 23], [5, 39], [6, 45], [7, 42], [8, 49], [9, 36], [10, 33]], then print its coefficients, inflections, total accumulation over its range, and correlation
>>> model_agnostic = linear_model([[1, 32], [2, 25], [3, 14], [4, 23], [5, 39], [6, 45], [7, 42], [8, 49], [9, 36], [10, 33]])
>>> print(model_agnostic['constants'])
[1.9636, 23.0]
>>> print(model_agnostic['points']['inflections'])
[None]
>>> print(model_agnostic['accumulations']['range'])
304.1982
>>> print(model_agnostic['correlation'])
0.5516
"""
# Handle input errors
matrix_of_scalars(data, 'first')
long_vector(data)
positive_integer(precision)
# Store independent and dependent variable values separately
independent_variable = single_dimension(data, 1)
dependent_variable = single_dimension(data, 2)
# Create matrices for independent and dependent variables
independent_matrix = []
dependent_matrix = column_conversion(dependent_variable)
# Iterate over inputted data
for element in independent_variable:
# Store linear and constant evaluations of original independent elements together as lists within independent matrix
independent_matrix.append([element, 1])
# Solve system of equations
solution = system_solution(independent_matrix, dependent_matrix, precision)
# Eliminate zeroes from solution
coefficients = no_zeroes(solution, precision)
# Generate evaluations for function, derivatives, and integral
equation = linear_equation(*coefficients, precision)
derivative = linear_derivatives(*coefficients, precision)['first']['evaluation']
integral = linear_integral(*coefficients, precision)['evaluation']
# Determine key points of graph
points = key_coordinates('linear', coefficients, precision)
# Generate values for lower and upper bounds
five_numbers = five_number_summary(independent_variable, precision)
min_value = five_numbers['minimum']
max_value = five_numbers['maximum']
q1 = five_numbers['q1']
q3 = five_numbers['q3']
# Calculate accumulations
accumulated_range = accumulated_area('linear', coefficients, min_value, max_value, precision)
accumulated_iqr = accumulated_area('linear', coefficients, q1, q3, precision)
# Determine average values and their points
averages_range = average_values('linear', coefficients, min_value, max_value, precision)
averages_iqr = average_values('linear', coefficients, q1, q3, precision)
# Create list of predicted outputs
predicted = []
for element in independent_variable:
predicted.append(equation(element))
# Calculate correlation coefficient for model
accuracy = correlation_coefficient(dependent_variable, predicted, precision)
# Package preceding results in multiple dictionaries
evaluations = {
'equation': equation,
'derivative': derivative,
'integral': integral
}
points = {
'roots': points['roots'],
'maxima': points['maxima'],
'minima': points['minima'],
'inflections': points['inflections']
}
accumulations = {
'range': accumulated_range,
'iqr': accumulated_iqr
}
averages = {
'range': averages_range,
'iqr': averages_iqr
}
# Package all dictionaries in single dictionary to return
result = {
'constants': coefficients,
'evaluations': evaluations,
'points': points,
'accumulations': accumulations,
'averages': averages,
'correlation': accuracy
}
return result
def sinusoidal_equation(first_constant, second_constant, third_constant, fourth_constant, precision = 4):
"""
Generates a sinusoidal function to provide evaluations at variable inputs
Parameters
----------
first_constant : int or float
Vertical stretch factor of the resultant sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
second_constant : int or float
Horizontal stretch factor of the resultant sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
third_constant : int or float
Horizontal shift of the resultant sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
fourth_constant : int or float
Vertical shift of the resultant sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the resultant roots
Raises
------
TypeError
First four arguments must be integers or floats
ValueError
Last argument must be a positive integer
Returns
-------
evaluation : func
Function for evaluating a sinusoidal equation when passed any integer or float argument
See Also
--------
:func:`~regressions.analyses.derivatives.sinusoidal.sinusoidal_derivatives`, :func:`~regressions.analyses.integrals.sinusoidal.sinusoidal_integral`, :func:`~regressions.analyses.roots.sinusoidal.sinusoidal_roots`, :func:`~regressions.models.sinusoidal.sinusoidal_model`
Notes
-----
- Standard form of a sinusoidal function: :math:`f(x) = a\\cdot{\\sin(b\\cdot(x - c))} + d`
- Period of function: :math:`\\frac{2\\pi}{|b|}`
- Amplitude of function: :math:`|a|`
- |sine_functions|
Examples
--------
Import `sinusoidal_equation` function from `regressions` library
>>> from regressions.analyses.equations.sinusoidal import sinusoidal_equation
Create a sinusoidal function with coefficients 2, 3, 5, and 7, then evaluate it at 10
>>> evaluation_first = sinusoidal_equation(2, 3, 5, 7)
>>> print(evaluation_first(10))
8.3006
Create a sinusoidal function with coefficients 7, -5, -3, and 2, then evaluate it at 10
>>> evaluation_second = sinusoidal_equation(7, -5, -3, 2)
>>> print(evaluation_second(10))
-3.7878
Create a sinusoidal function with all inputs set to 0, then evaluate it at 10
>>> evaluation_zero = sinusoidal_equation(0, 0, 0, 0)
>>> print(evaluation_zero(10))
0.0001
"""
# Handle input errors
four_scalars(first_constant, second_constant, third_constant, fourth_constant)
positive_integer(precision)
coefficients = no_zeroes([first_constant, second_constant, third_constant, fourth_constant], precision)
# Create evaluation
def sinusoidal_evaluation(variable):
evaluation = coefficients[0] * sin(coefficients[1] * (variable - coefficients[2])) + coefficients[3]
result = rounded_value(evaluation, precision)
return result
return sinusoidal_evaluation
def hyperbolic_integral(first_constant, second_constant, precision=4):
"""
Generates the integral of a hyperbolic function
Parameters
----------
first_constant : int or float
Coefficient of the reciprocal variable of the original hyperbolic function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
second_constant : int or float
Coefficient of the constant term of the original hyperbolic function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the resultant roots
Raises
------
TypeError
First two arguments must be integers or floats
ValueError
Last argument must be a positive integer
Returns
-------
integral['constants'] : list of float
Coefficients of the resultant integral
integral['evaluation'] : func
Function for evaluating the resultant integral at any float or integer argument; if zero inputted as argument, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
See Also
--------
:func:`~regressions.analyses.equations.hyperbolic.hyperbolic_equation`, :func:`~regressions.analyses.derivatives.hyperbolic.hyperbolic_derivatives`, :func:`~regressions.analyses.roots.hyperbolic.hyperbolic_roots`, :func:`~regressions.models.hyperbolic.hyperbolic_model`
Notes
-----
- Standard form of a hyperbolic function: :math:`f(x) = a\\cdot{\\frac{1}{x}} + b`
- Integral of a hyperbolic function: :math:`F(x) = a\\cdot{\\ln|x|} + b\\cdot{x}`
- |indefinite_integral|
- |integration_formulas|
Examples
--------
Import `sinusoidal_hyperbolic` function from `regressions` library
>>> from regressions.analyses.hyperbolics.sinusoidal import sinusoidal_hyperbolic
Generate the integral of a hyperbolic function with coefficients 2 and 3, then display its coefficients
>>> integral_constants = hyperbolic_integral(2, 3)
>>> print(integral_constants['constants'])
[2.0, 3.0]
Generate the integral of a hyperbolic function with coefficients -2 and 3, then evaluate its integral at 10
>>> integral_evaluation = hyperbolic_integral(-2, 3)
>>> print(integral_evaluation['evaluation'](10))
25.3948
Generate the integral of a hyperbolic function with all inputs set to 0, then display its coefficients
>>> integral_zeroes = hyperbolic_integral(0, 0)
>>> print(integral_zeroes['constants'])
[0.0001, 0.0001]
"""
# Handle input errors
two_scalars(first_constant, second_constant)
positive_integer(precision)
coefficients = no_zeroes([first_constant, second_constant], precision)
# Create constants
constants = [coefficients[0], coefficients[1]]
# Create evaluation
def hyperbolic_evaluation(variable):
# Circumvent logarithm of zero
if variable == 0:
variable = 10**(-precision)
evaluation = constants[0] * log(
abs(variable)) + constants[1] * variable
rounded_evaluation = rounded_value(evaluation, precision)
return rounded_evaluation
# Package constants and evaluation in single dictionary
results = {'constants': constants, 'evaluation': hyperbolic_evaluation}
return results
def coordinate_pairs(equation_type, coefficients, inputs, point_type = 'point', precision = 4):
"""
Creates a list of coordinate pairs from a set of inputs
Parameters
----------
equation_type : str
Name of the type of function for which coordinate pairs must be determined (e.g., 'linear', 'quadratic')
coefficients : list of int or float
Coefficients to use to generate the equation to investigate
inputs : list of int or float or str
X-coordinates to use to generate the y-coordinates for each coordinate pair
point_type : str, default='point'
Name of the type of point that describes all points which must be generated (e.g., 'intercepts', 'maxima')
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the results
Raises
------
ValueError
First argument must be either 'linear', 'quadratic', 'cubic', 'hyperbolic', 'exponential', 'logarithmic', 'logistic', or 'sinusoidal'
TypeError
Second argument must be a 1-dimensional list containing elements that are integers or floats
TypeError
Third argument must be a 1-dimensional list containing elements that are integers, floats, strings, or None
ValueError
Fourth argument must be either 'point', 'intercepts', 'maxima', 'minima', or 'inflections'
ValueError
Last argument must be a positive integer
Returns
-------
points : list of float or str
List containing lists of coordinate pairs, in which the second element of the inner lists are floats and the first elements of the inner lists are either floats or strings (the latter for general forms); may return a list of None if inputs list contained None
See Also
--------
:func:`~regressions.vectors.generate.generate_elements`, :func:`~regressions.vectors.unify.unite_vectors`
Notes
-----
- Set of x-coordinates of points: :math:`x_i = \\{ x_1, x_2, \\cdots, x_n \\}`
- Set of y-coordinates of points: :math:`y_i = \\{ y_1, y_2, \\cdots, y_n \\}`
- Set of coordinate pairs of points: :math:`p_i = \\{ (x_1, y_1), (x_2, y_2), \\cdots, (x_n, y_n) \\}`
Examples
--------
Import `coordinate_pairs` function from `regressions` library
>>> from regressions.analyses.points import coordinate_pairs
Generate a list of coordinate pairs for a cubic function with coefficients 2, 3, 5, and 7 based off x-coordinates of 1, 2, 3, and 4
>>> points_cubic = coordinate_pairs('cubic', [2, 3, 5, 7], [1, 2, 3, 4])
>>> print(points_cubic)
[[1.0, 17.0], [2.0, 45.0], [3.0, 103.0], [4.0, 203.0]]
Generate a list of coordinate pairs for a sinusoidal function with coefficients 2, 3, 5, and 7 based off x-coordinates of 1, 2, 3, and 4
>>> points_sinusoidal = coordinate_pairs('sinusoidal', [2, 3, 5, 7], [1, 2, 3, 4])
>>> print(points_sinusoidal)
[[1.0, 8.0731], [2.0, 6.1758], [3.0, 7.5588], [4.0, 6.7178]]
Generate a list of coordinate pairs for a quadratic function with coefficients 1, -5, and 6 based off x-coordinates of 2 and 3 (given that the resultant coordinates will be x-intercepts)
>>> points_quadratic = coordinate_pairs('quadratic', [1, -5, 6], [2, 3], 'intercepts')
>>> print(points_quadratic)
[[2.0, 0.0], [3.0, 0.0]]
"""
# Handle input errors
select_equations(equation_type)
vector_of_scalars(coefficients, 'second')
allow_none_vector(inputs, 'third')
select_points(point_type, 'fourth')
positive_integer(precision)
# Create equations for evaluating inputs (based on equation type)
equation = lambda x : x
if equation_type == 'linear':
equation = linear_equation(*coefficients, precision)
elif equation_type == 'quadratic':
equation = quadratic_equation(*coefficients, precision)
elif equation_type == 'cubic':
equation = cubic_equation(*coefficients, precision)
elif equation_type == 'hyperbolic':
equation = hyperbolic_equation(*coefficients, precision)
elif equation_type == 'exponential':
equation = exponential_equation(*coefficients, precision)
elif equation_type == 'logarithmic':
equation = logarithmic_equation(*coefficients, precision)
elif equation_type == 'logistic':
equation = logistic_equation(*coefficients, precision)
elif equation_type == 'sinusoidal':
equation = sinusoidal_equation(*coefficients, precision)
# Round inputs
rounded_inputs = []
for point in inputs:
if isinstance(point, (int, float)):
rounded_inputs.append(rounded_value(point, precision))
else:
rounded_inputs.append(point)
# Create empty lists
outputs = []
coordinates = []
# Handle no points
if rounded_inputs[0] == None:
coordinates.append(None)
# Fill outputs list with output value at each input
else:
for value in rounded_inputs:
# Circumvent inaccurate rounding
if point_type == 'intercepts':
outputs.append(0.0)
# Evaluate function at inputs
else:
# Evaluate numerical inputs
if isinstance(value, (int, float)):
output = equation(value)
rounded_output = rounded_value(output, precision)
outputs.append(rounded_output)
# Handle non-numerical inputs
else:
outputs.append(outputs[0])
# Unite inputs and outputs for maxima into single list
coordinates.extend(unite_vectors(rounded_inputs, outputs))
# Return final coordinate pairs
return coordinates
def key_coordinates(equation_type, coefficients, precision = 4):
"""
Calculates the key points of a specific function
Parameters
----------
equation_type : str
Name of the type of function for which key points must be determined (e.g., 'linear', 'quadratic')
coefficients : list of int or float
Coefficients to use to generate the equation to investigate
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the results
Raises
------
ValueError
First argument must be either 'linear', 'quadratic', 'cubic', 'hyperbolic', 'exponential', 'logarithmic', 'logistic', or 'sinusoidal'
TypeError
Second argument must be a 1-dimensional list containing elements that are integers or floats
ValueError
Last argument must be a positive integer
Returns
-------
points['roots'] : list of float or str
List containing two-element lists for each point; first elements of those lists will be the value of the x-coordinate at which the original function has a root; second elements of those lists will be 0; if the function is sinusoidal, then only the initial results within a four-period interval will be listed, but general forms will also be included; if the function has no roots, then it will return a list of `None`
points['maxima'] : list of float or str
List containing two-element lists for each point; first elements of those lists will be the value of the x-coordinate at which the original function has a relative maximum; second elements of those lists will be the y-coordinate of that maximum; if the function is sinusoidal, then only the initial results within a two-period interval will be listed, but a general form will also be included; if the function has no maxima, then it will return a list of `None`
points['minima'] : list of float or str
List containing two-element lists for each point; first elements of those lists will be the value of the x-coordinate at which the original function has a relative minimum; second elements of those lists will be the y-coordinate of that minimum; if the function is sinusoidal, then only the initial results within a two-period interval will be listed, but a general form will also be included; if the function has no minima, then it will return a list of `None`
points['inflections'] : list of float or str
List containing two-element lists for each point; first elements of those lists will be the value of the x-coordinate at which the original function has an inflection; second elements of those lists will be the y-coordinate of that inflection; if the function is sinusoidal, then only the initial results within a two-period interval will be listed, but a general form will also be included; if the function has no inflection points, then it will return a list of `None`
See Also
--------
- Roots for key functions: :func:`~regressions.analyses.roots.linear.linear_roots`, :func:`~regressions.analyses.roots.quadratic.quadratic_roots`, :func:`~regressions.analyses.roots.cubic.cubic_roots`, :func:`~regressions.analyses.roots.hyperbolic.hyperbolic_roots`, :func:`~regressions.analyses.roots.exponential.exponential_roots`, :func:`~regressions.analyses.roots.logarithmic.logarithmic_roots`, :func:`~regressions.analyses.roots.logistic.logistic_roots`, :func:`~regressions.analyses.roots.sinusoidal.sinusoidal_roots`
- Graphical analysis: :func:`~regressions.analyses.criticals.critical_points`, :func:`~regressions.analyses.intervals.sign_chart`, :func:`~regressions.analyses.maxima.maxima_points`, :func:`~regressions.analyses.minima.minima_points`, :func:`~regressions.analyses.extrema.extrema_points`, :func:`~regressions.analyses.inflections.inflection_points`
Notes
-----
- Key points include x-intercepts, maxima, minima, and points of inflection
- |intercepts|
- |extrema|
- |inflections|
Examples
--------
Import `key_coordinates` function from `regressions` library
>>> from regressions.analyses.points import key_coordinates
Calculate the key points of a cubic function with coefficients 1, -15, 63, and -7
>>> points_cubic = key_coordinates('cubic', [1, -15, 63, -7])
>>> print(points_cubic['roots'])
[[0.1142, 0.0]]
>>> print(points_cubic['maxima'])
[[3.0, 74.0]]
>>> print(points_cubic['minima'])
[[7.0, 42.0]]
>>> print(points_cubic['inflections'])
[[5.0, 58.0]]
Calculate the key points of a sinusoidal function with coefficients 2, 3, 5, and 1
>>> points_sinusoidal = key_coordinates('sinusoidal', [2, 3, 5, 1])
>>> print(points_sinusoidal['roots'])
[[4.8255, 0.0], [6.2217, 0.0], [6.9199, 0.0], [8.3161, 0.0], [9.0143, 0.0], [10.4105, 0.0], [11.1087, 0.0], [12.5049, 0.0], [13.203, 0.0], [14.5993, 0.0], ['4.8255 + 2.0944k', 0.0], ['6.2217 + 2.0944k', 0.0]]
>>> print(points_sinusoidal['maxima'])
[[5.5236, 3.0], [7.618, 3.0], [9.7124, 3.0], ['5.5236 + 2.0944k', 3.0]]
>>> print(points_sinusoidal['minima'])
[[6.5708, -1.0], [8.6652, -1.0], ['6.5708 + 2.0944k', -1.0]]
>>> print(points_sinusoidal['inflections'])
[[5.0, 1.0], [6.0472, 1.0], [7.0944, 1.0], [8.1416, 1.0], [9.1888, 1.0001], ['5.0 + 1.0472k', 1.0]]
"""
# Handle input errors
select_equations(equation_type)
vector_of_scalars(coefficients, 'second')
positive_integer(precision)
# Create lists of inputs
intercepts_inputs = intercept_points(equation_type, coefficients, precision)
extrema_inputs = extrema_points(equation_type, coefficients, precision)
maxima_inputs = extrema_inputs['maxima']
minima_inputs = extrema_inputs['minima']
inflections_inputs = inflection_points(equation_type, coefficients, precision)
# Generate coordinate pairs for all x-intercepts
intercepts_coordinates = coordinate_pairs(equation_type, coefficients, intercepts_inputs, 'intercepts', precision)
# Generate coordinate pairs for all maxima
maxima_coordinates = coordinate_pairs(equation_type, coefficients, maxima_inputs, 'maxima', precision)
# Generate coordinate pairs for all minima
minima_coordinates = coordinate_pairs(equation_type, coefficients, minima_inputs, 'minima', precision)
# Generate coordinate pairs for all points of inflection
inflections_coordinates = coordinate_pairs(equation_type, coefficients, inflections_inputs, 'inflections', precision)
# Create dictionary to return
result = {
'roots': intercepts_coordinates,
'maxima': maxima_coordinates,
'minima': minima_coordinates,
'inflections': inflections_coordinates
}
return result
def sinusoidal_derivatives(first_constant,
second_constant,
third_constant,
fourth_constant,
precision=4):
"""
Calculates the first and second derivatives of a sinusoidal function
Parameters
----------
first_constant : int or float
Vertical stretch factor of the original sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
second_constant : int or float
Horizontal stretch factor of the original sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
third_constant : int or float
Horizontal shift of the original sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
fourth_constant : int or float
Vertical shift of the original sine function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the resultant roots
Raises
------
TypeError
First four arguments must be integers or floats
ValueError
Last argument must be a positive integer
Returns
-------
derivatives['first']['constants'] : list of float
Coefficients of the resultant first derivative
derivatives['first']['evaluation'] : func
Function for evaluating the resultant first derivative at any float or integer argument
derivatives['second']['constants'] : list of float
Coefficients of the resultant second derivative
derivatives['second']['evaluation'] : func
Function for evaluating the resultant second derivative at any float or integer argument
See Also
--------
:func:`~regressions.analyses.equations.sinusoidal.sinusoidal_equation`, :func:`~regressions.analyses.integrals.sinusoidal.sinusoidal_integral`, :func:`~regressions.analyses.roots.sinusoidal.sinusoidal_roots`, :func:`~regressions.models.sinusoidal.sinusoidal_model`
Notes
-----
- Standard form of a sinusoidal function: :math:`f(x) = a\\cdot{\\sin(b\\cdot(x - c))} + d`
- First derivative of a sinusoidal function: :math:`f'(x) = ab\\cdot{\\cos(b\\cdot(x - c))}`
- Second derivative of a sinusoidal function: :math:`f''(x) = -ab^2\\cdot{\\sin(b\\cdot(x - c))}`
- |differentiation_formulas|
- |chain_rule|
- |trigonometric|
Examples
--------
Import `sinusoidal_derivatives` function from `regressions` library
>>> from regressions.analyses.derivatives.sinusoidal import sinusoidal_derivatives
Generate the derivatives of a sinusoidal function with coefficients 2, 3, 5, and 7, then display the coefficients of its first and second derivatives
>>> derivatives_constants = sinusoidal_derivatives(2, 3, 5, 7)
>>> print(derivatives_constants['first']['constants'])
[6.0, 3.0, 5.0]
>>> print(derivatives_constants['second']['constants'])
[-18.0, 3.0, 5.0]
Generate the derivatives of a sinusoidal function with coefficients 7, -5, -3, and 2, then evaluate its first and second derivatives at 10
>>> derivatives_evaluation = sinusoidal_derivatives(7, -5, -3, 2)
>>> print(derivatives_evaluation['first']['evaluation'](10))
19.6859
>>> print(derivatives_evaluation['second']['evaluation'](10))
144.695
Generate the derivatives of a sinusoidal function with all inputs set to 0, then display the coefficients of its first and second derivatives
>>> derivatives_zeroes = sinusoidal_derivatives(0, 0, 0, 0)
>>> print(derivatives_zeroes['first']['constants'])
[0.0001, 0.0001, 0.0001]
>>> print(derivatives_zeroes['second']['constants'])
[-0.0001, 0.0001, 0.0001]
"""
# Handle input errors
four_scalars(first_constant, second_constant, third_constant,
fourth_constant)
positive_integer(precision)
coefficients = no_zeroes(
[first_constant, second_constant, third_constant, fourth_constant],
precision)
# Create first derivative
first_coefficients = [
coefficients[0] * coefficients[1], coefficients[1], coefficients[2]
]
first_constants = rounded_list(first_coefficients, precision)
def first_derivative(variable):
evaluation = first_constants[0] * cos(first_constants[1] *
(variable - first_constants[2]))
rounded_evaluation = rounded_value(evaluation, precision)
return rounded_evaluation
first_dictionary = {
'constants': first_constants,
'evaluation': first_derivative
}
# Create second derivative
second_coefficients = [
-1 * first_constants[0] * first_constants[1], first_constants[1],
first_constants[2]
]
second_constants = rounded_list(second_coefficients, precision)
def second_derivative(variable):
evaluation = second_constants[0] * sin(
second_constants[1] * (variable - second_constants[2]))
rounded_evaluation = rounded_value(evaluation, precision)
return rounded_evaluation
second_dictionary = {
'constants': second_constants,
'evaluation': second_derivative
}
# Package both derivatives in single dictionary
results = {'first': first_dictionary, 'second': second_dictionary}
return results
def hyperbolic_derivatives(first_constant, second_constant, precision=4):
"""
Calculates the first and second derivatives of a hyperbolic function
Parameters
----------
first_constant : int or float
Coefficient of the reciprocal variable of the original hyperbolic function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
second_constant : int or float
Coefficient of the constant term of the original hyperbolic function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the resultant roots
Raises
------
TypeError
First two arguments must be integers or floats
ValueError
Last argument must be a positive integer
Returns
-------
derivatives['first']['constants'] : list of float
Coefficients of the resultant first derivative
derivatives['first']['evaluation'] : func
Function for evaluating the resultant first derivative at any float or integer argument; if zero inputted as argument, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
derivatives['second']['constants'] : list of float
Coefficients of the resultant second derivative
derivatives['second']['evaluation'] : func
Function for evaluating the resultant second derivative at any float or integer argument; if zero inputted as argument, it will be converted to a small, non-zero decimal value (e.g., 0.0001)
See Also
--------
:func:`~regressions.analyses.equations.hyperbolic.hyperbolic_equation`, :func:`~regressions.analyses.integrals.hyperbolic.hyperbolic_integral`, :func:`~regressions.analyses.roots.hyperbolic.hyperbolic_roots`, :func:`~regressions.models.hyperbolic.hyperbolic_model`
Notes
-----
- Standard form of a hyperbolic function: :math:`f(x) = a\\cdot{\\frac{1}{x}} + b`
- First derivative of a hyperbolic function: :math:`f'(x) = -a\\cdot{\\frac{1}{x^2}}`
- Second derivative of a hyperbolic function: :math:`f''(x) = 2a\\cdot{\\frac{1}{x^3}}`
- |differentiation_formulas|
Examples
--------
Import `hyperbolic_derivatives` function from `regressions` library
>>> from regressions.analyses.derivatives.hyperbolic import hyperbolic_derivatives
Generate the derivatives of a hyperbolic function with coefficients 2 and 3, then display the coefficients of its first and second derivatives
>>> derivatives_constants = hyperbolic_derivatives(2, 3)
>>> print(derivatives_constants['first']['constants'])
[-2.0]
>>> print(derivatives_constants['second']['constants'])
[4.0]
Generate the derivatives of a hyperbolic function with coefficients -2 and 3, then evaluate its first and second derivatives at 10
>>> derivatives_evaluation = hyperbolic_derivatives(-2, 3)
>>> print(derivatives_evaluation['first']['evaluation'](10))
0.02
>>> print(derivatives_evaluation['second']['evaluation'](10))
-0.004
Generate the derivatives of a hyperbolic function with all inputs set to 0, then display the coefficients of its first and second derivatives
>>> derivatives_zeroes = hyperbolic_derivatives(0, 0)
>>> print(derivatives_zeroes['first']['constants'])
[-0.0001]
>>> print(derivatives_zeroes['second']['constants'])
[0.0002]
"""
# Handle input errors
two_scalars(first_constant, second_constant)
positive_integer(precision)
coefficients = no_zeroes([first_constant, second_constant], precision)
# Create first derivative
first_constants = [-1 * coefficients[0]]
def first_derivative(variable):
# Circumvent division by zero
if variable == 0:
variable = 10**(-precision)
evaluation = first_constants[0] / variable**2
rounded_evaluation = rounded_value(evaluation, precision)
return rounded_evaluation
first_dictionary = {
'constants': first_constants,
'evaluation': first_derivative
}
# Create second derivative
second_constants = [-2 * first_constants[0]]
def second_derivative(variable):
# Circumvent division by zero
if variable == 0:
variable = 10**(-precision)
evaluation = second_constants[0] / variable**3
rounded_evaluation = rounded_value(evaluation, precision)
return rounded_evaluation
second_dictionary = {
'constants': second_constants,
'evaluation': second_derivative
}
# Package both derivatives in single dictionary
results = {'first': first_dictionary, 'second': second_dictionary}
return results