def test_compare_vectors_3_4_raises(self):
with self.assertRaises(Exception) as context:
compare_vectors(first_vector, longer_vector)
self.assertEqual(type(context.exception), ValueError)
self.assertEqual(
str(context.exception),
'Both arguments must contain the same number of elements')
def component_form(initial_point, terminal_point):
"""
Calculates the component form for a vector by using two points
Parameters
----------
initial_point : list of int or float
List of numbers representing a point
terminal_point : list of int or float
List of numbers representing a point
Raises
------
TypeError
Arguments must be 1-dimensional lists
TypeError
Elements of arguments must be integers or floats
ValueError
Both arguments must contain the same number of elements
Returns
-------
components : list of int or float
List in which each element is the difference of the corresponding elements from the input points (specifically, the change from the initial point to the terminal point)
See Also
--------
:func:`~regressions.vectors.addition.vector_sum`, :func:`~regressions.vectors.multiplication.scalar_product_vector`, :func:`~regressions.vectors.direction.vector_direction`, :func:`~regressions.vectors.magnitude.vector_magnitude`
Notes
-----
- Initial point: :math:`A = (a_1, a_2, \\cdots, a_n)`
- Terminal point: :math:`B = (b_1, b_2, \\cdots, b_n)`
- Component form of vector: :math:`\\overrightarrow{AB} = \\langle b_1 - a_1, b_2 - a_2, \\cdots, b_n - a_n \\rangle`
- |component_form|
Examples
--------
Import `component_form` function from `regressions` library
>>> from regressions.vectors.components import component_form
Determine the component form of a vector with an initial point of [1, 2, 3] and a terminal point of [4, 5, 6]
>>> components_3d = component_form([1, 2, 3], [4, 5, 6])
>>> print(components_3d)
[3, 3, 3]
Determine the component form of a vector with an initial point of [-5, 12] and a terminal point of [3, -7]
>>> components_2d = component_form([-5, 12], [3, -7])
>>> print(components_2d)
[8, -19]
"""
# Handle input errors
compare_vectors(initial_point, terminal_point)
# Determine difference between terminal point and initial point
result = vector_sum(terminal_point,
scalar_product_vector(initial_point, -1))
return result
def multiple_residuals(actual_array, expected_array):
"""
Generates a list of the differences between the actual values from one list and the expected values from another list
Parameters
----------
actual_array : list of int or float
List containing the actual values observed from a data set
expected_array : list of int or float
List containing the expected values predicted for a data set
Raises
------
TypeError
Arguments must be 1-dimensional lists
TypeError
Elements of arguments must be integers or floats
ValueError
Both arguments must contain the same number of elements
Returns
-------
residuals : list of float
Differences between the actual values and the expected values
See Also
--------
:func:`~regressions.statistics.deviations.multiple_deviations`, :func:`~regressions.statistics.correlation.correlation_coefficient`
Notes
-----
- Observed values: :math:`y_i = \\{ y_1, y_2, \\cdots, y_n \\}`
- Predicted values: :math:`\\hat{y}_i = \\{ \\hat{y}_1, \\hat{y}_2, \\cdots, \\hat{y}_n \\}`
- Residuals: :math:`e_i = \\{ y_1 - \\hat{y}_1, y_2 - \\hat{y}_2, \\cdots, y_n - \\hat{y}_n \\}`
- |residual|
Examples
--------
Import `multiple_residuals` function from `regressions` library
>>> from regressions.statistics.residuals import multiple_residuals
Determine the residuals between the actual values [5.6, 8.1, 6.3] and the expected values [6.03, 8.92, 6.12]
>>> residuals_short = multiple_residuals([5.6, 8.1, 6.3], [6.03, 8.92, 6.12])
>>> print(residuals_short)
[-0.4300000000000006, -0.8200000000000003, 0.17999999999999972]
Determine the residuals between the actual values [11.7, 5.6, 8.1, 13.4, 6.3] and the expected values [15.17, 6.03, 8.92, 9.42, 6.12]
>>> residuals_long = multiple_residuals([11.7, 5.6, 8.1, 13.4, 6.3], [15.17, 6.03, 8.92, 9.42, 6.12])
>>> print(residuals_long)
[-3.4700000000000006, -0.4300000000000006, -0.8200000000000003, 3.9800000000000004, 0.17999999999999972]
"""
# Handle input errors
compare_vectors(actual_array, expected_array)
# Create list to return
results = []
# Iterate over inputs
for i in range(len(actual_array)):
# Store residuals of corresponding elements in list to return
results.append(single_residual(actual_array[i], expected_array[i]))
# Return results
return results
def correlation_coefficient(actuals, expecteds, precision=4):
"""
Calculates the correlation coefficient as a way to predict the strength of a predicted model by comparing the ratio of residuals to deviations, in order to determine a strong or weak relationship
Parameters
----------
actuals : list of int or float
List containing the actual values observed from a data set
expecteds : list of int or float
List containing the expected values for a data set based on a predictive model
precision : int, default=4
Maximum number of digits that can appear after the decimal place of the result
Raises
------
TypeError
First and second arguments must be 1-dimensional lists
TypeError
Elements of first and second arguments must be integers or floats
ValueError
First and second arguments must contain the same number of elements
ValueError
Last argument must be a positive integer
Returns
-------
correlation : float
Number indicating statistical strenght of the relationship between two variables; the closer to 1, the stronger; the closer to 0, the weaker
See Also
--------
:func:`~regressions.statistics.residuals.multiple_residuals`, :func:`~regressions.statistics.deviations.multiple_deviations`, :func:`~regressions.statistics.summation.sum_value`
Notes
-----
- Observed values: :math:`y_i = \\{ y_1, y_2, \\cdots, y_n \\}`
- Predicted values: :math:`\\hat{y}_i = \\{ \\hat{y}_1, \\hat{y}_2, \\cdots, \\hat{y}_n \\}`
- Mean of all observed values: :math:`\\bar{y} = \\frac{1}{n}\\cdot{\\sum\\limits_{i=1}^n y_i}`
- Residuals: :math:`e_i = \\{ y_1 - \\hat{y}_1, y_2 - \\hat{y}_2, \\cdots, y_n - \\hat{y}_n \\}`
- Deviations: :math:`d_i = \\{ y_1 - \\bar{y}, y_2 - \\bar{y}, \\cdots, y_n - \\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: :math:`r = \\sqrt{1 - \\frac{SS_{res}}{SS_{dev}}}`
- |determination|
Examples
--------
Import `correlation_coefficient` function from `regressions` library
>>> from regressions.statistics.correlation import correlation_coefficient
Calculate the correlation using the provided actual values [8.2, 9.41, 1.23, 34.7] and the predicted values [7.863, 8.9173, 2.0114, 35.8021]
>>> correlation_short = correlation_coefficient([8.2, 9.41, 1.23, 34.7], [7.863, 8.9173, 2.0114, 35.8021])
>>> print(correlation_short)
0.9983
Calculate the correlation using the provided actual values [2, 3, 5, 7, 11, 13, 17, 19] and the predicted values [1.0245, 3.7157, 6.1398, 8.1199, 12.7518, 14.9621, 15.2912, 25.3182]
>>> correlation_long = correlation_coefficient([2, 3, 5, 7, 11, 13, 17, 19], [1.0245, 3.7157, 6.1398, 8.1199, 12.7518, 14.9621, 15.2912, 25.3182])
>>> print(correlation_long)
0.9011
"""
# Handle input errors
compare_vectors(actuals, expecteds)
positive_integer(precision)
# Determine residuals and deviations between actuals and expecteds
residuals = multiple_residuals(actuals, expecteds)
deviations = multiple_deviations(actuals)
# Square residuals and deviations
squared_residuals = []
for residual in residuals:
squared_residuals.append(residual**2)
squared_deviations = []
for deviation in deviations:
squared_deviations.append(deviation**2)
# Calculate the sums of the squares of the residuals and deviations
residual_sum = sum_value(squared_residuals)
deviation_sum = sum_value(squared_deviations)
# Circumvent division by zero
if deviation_sum == 0:
deviation_sum = 10**(-precision)
# Calculate key ratio
ratio = residual_sum / deviation_sum
# Handle no solution
if ratio > 1:
return 0.0
# Handle general case
else:
result = (1 - ratio)**(1 / 2)
return rounded_value(result, precision)
def unite_vectors(vector_one, vector_two):
"""
Unites two row vectors into a single matrix whose columns coincide with the input vectors
Parameters
----------
vector_one : list of int or float
List of numbers representing a vector
vector_two : list of int or float
List of numbers representing a vector
Raises
------
TypeError
Arguments must be 1-dimensional lists
TypeError
Elements of arguments must be integers or floats
ValueError
Both arguments must contain the same number of elements
Returns
-------
matrix : list of lists of int or float
List containing lists; length of outer list will equal lengths of supplied vectors; length of inner lists will equal two
See Also
--------
:func:`~regressions.vectors.dimension.single_dimension`, :func:`~regressions.statistics.halve.partition`
Notes
-----
- First vector: :math:`\\langle a_1, a_2, \\cdots, a_n \\rangle`
- Second vector: :math:`\\langle b_1, b_2, \\cdots, b_n \\rangle`
- Matrix unifying first and second vectors: :math:`\\begin{bmatrix} a_1 & b_1 \\\\ a_2 & b_2 \\\\ \\cdots & \\cdots \\\\ a_n & b_n \\end{bmatrix}`
Examples
--------
Import `unite_vectors` function from `regressions` library
>>> from regressions.vectors.unify import unite_vectors
Unite [1, 2, 3] and [4, 5, 6]
>>> matrix_3x2 = unite_vectors([1, 2, 3], [4, 5, 6])
>>> print(matrix_2x3)
[[1, 4], [2, 5], [3, 6]]
Unite [-5, 12] and [3, -7]
>>> matrix_2x2 = unite_vectors([-5, 12], [3, -7])
>>> print(matrix_2x2)
[[-5, 3], [12, -7]]
"""
# Handle input errors
compare_vectors(vector_one, vector_two)
# Create list to return
result = []
# Handle no solution
if vector_one[0] == None:
result.append(None)
# Handle general case
else:
# Iterate over inputs
for i in range(len(vector_one)):
# Store corresponding elements from inputs as lists within list to return
result.append([vector_one[i], vector_two[i]])
# Return result
return result
def test_compare_vectors_3_3(self):
compare_vectors_3_3 = compare_vectors(first_vector, second_vector)
self.assertEqual(compare_vectors_3_3,
'Both arguments contain the same number of elements')
def vector_sum(vector_one, vector_two):
"""
Calculates the sum of two vectors
Parameters
----------
vector_one : list of int or float
List of numbers representing a vector
vector_two : list of int or float
List of numbers representing a vector
Raises
------
TypeError
Arguments must be 1-dimensional lists
TypeError
Elements of arguments must be integers or floats
ValueError
Both arguments must contain the same number of elements
Returns
-------
vector : list of int or float
List in which each element is the sum of the corresponding elements from the input vectors
See Also
--------
:func:`~regressions.matrices.addition.matrix_sum`, :func:`~regressions.vectors.multiplication.scalar_product_vector`
Notes
-----
- First vector: :math:`\\mathbf{a} = \\langle a_1, a_2, \\cdots, a_n \\rangle`
- Second vector: :math:`\\mathbf{b} = \\langle b_1, b_2, \\cdots, b_n \\rangle`
- Sum of vectors: :math:`\\mathbf{a} + \\mathbf{b} = \\langle a_1 + b_1, a_2 + b_2, \\cdots, a_n + b_n \\rangle`
- |vector_addition|
Examples
--------
Import `vector_sum` function from `regressions` library
>>> from regressions.vectors.addition import vector_sum
Add [1, 2, 3] and [4, 5, 6]
>>> vector_3d = vector_sum([1, 2, 3], [4, 5, 6])
>>> print(vector_3d)
[5, 7, 9]
Add [-5, 12] and [3, -7]
>>> vector_2d = vector_sum([-5, 12], [3, -7])
>>> print(vector_2d)
[-2, 5]
"""
# Handle input errors
compare_vectors(vector_one, vector_two)
# Create list to return
result = []
# Iterate over first input
for i in range(len(vector_one)):
# Store sums of corresponding vector elements in result
result.append(vector_one[i] + vector_two[i])
# Return result
return result
def dot_product(vector_one, vector_two):
"""
Calculates the product of two vectors
Parameters
----------
vector_one : list of int or float
List of numbers representing a vector
vector_two : list of int or float
List of numbers representing a vector
Raises
------
TypeError
Arguments must be 1-dimensional lists
TypeError
Elements of arguments must be integers or floats
ValueError
Both arguments must contain the same number of elements
Returns
-------
product : float
Number created by summing the products of the corresponding terms from each input vector
See Also
--------
:func:`~regressions.matrices.multiplication.matrix_product`
Notes
-----
- First vector: :math:`\\mathbf{a} = \\langle a_1, a_2, \\cdots, a_n \\rangle`
- Second vector: :math:`\\mathbf{b} = \\langle b_1, b_2, \\cdots, b_n \\rangle`:
- Dot product of vectors: :math:`\\mathbf{a}\\cdot{\\mathbf{b}} = a_1\\cdot{b_1} + a_2\\cdot{b_2} + \\cdots + a_n\\cdot{b_n}`
- |dot_product|
Examples
--------
Import `dot_product` function from `regressions` library
>>> from regressions.vectors.multiplication import dot_product
Multiply [1, 2, 3] and [4, 5, 6]
>>> product_3d = dot_product([1, 2, 3], [4, 5, 6])
>>> print(product_3d)
32.0
Multiply [-5, 12] and [3, -7]
>>> product_2d = dot_product([-5, 12], [3, -7])
>>> print(product_2d)
-99.0
"""
# Handle input errors
compare_vectors(vector_one, vector_two)
# Create intermediary number
result = 0
# Iterate over inputs
for i in range(len(vector_one)):
# Add products of corresponding elements from inputs to intermediary number
result += vector_one[i] * vector_two[i]
# Convert number to float
floated_result = float(result)
return floated_result