### Install miniKanren using pip

Source: https://github.com/pythological/kanren/blob/main/README.md

Installs the miniKanren library using the pip package installer. This is the standard method for installing Python packages.

```bash
pip install miniKanren
```

--------------------------------

### Unification Examples

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Demonstrates the `unify` function from the `logical-unification` library with various inputs and their resulting substitutions or `False` if unification fails.

```python
>>> unify((1, 2), (1, x), {})
{~x: 2}
>>> unify((1, 2), (1, x), {x: 2})
{~x: 2}
>>> unify((1, 2), (1, x), {x: 3})
False
```

--------------------------------

### Install development dependencies

Source: https://github.com/pythological/kanren/blob/main/README.md

Installs the necessary dependencies for developing the kanren library, as listed in the requirements.txt file. This is a common step after cloning a project's source code.

```bash
pip install -r requirements.txt
```

--------------------------------

### Set up pre-commit hooks

Source: https://github.com/pythological/kanren/blob/main/README.md

Installs pre-commit hooks for the kanren project. These hooks automate code quality checks and formatting before committing changes, ensuring consistency.

```bash
pre-commit install --install-hooks
```

--------------------------------

### Install miniKanren using conda

Source: https://github.com/pythological/kanren/blob/main/README.md

Installs the miniKanren library using the conda package manager, specifically from the conda-forge channel. This is an alternative installation method, often preferred in data science environments.

```bash
conda install -c conda-forge miniKanren
```

--------------------------------

### Reification Example

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Shows how the `reify` function transforms a term containing logic variables into a concrete term using a provided substitution.

```python
>>> reify((1, x), {x: 2})
(1, 2)
```

--------------------------------

### Multiple equality goals with multiple variables

Source: https://github.com/pythological/kanren/blob/main/README.md

Shows how to use multiple logic variables and equality goals simultaneously. This example finds a value `x` and `z` where `x` equals `z` and `z` equals 3.

```python
>>> from kanren import run, eq, var
>>> z = var()
>>> run(1, [x, z], eq(x, z),
...                    eq(z, 3))
([3, 3],)
```

--------------------------------

### Setup: Import Modules and Define etuple String Representation

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Imports necessary modules from kanren, unification, etuples, math, numbers, functools, and operator. It also defines a custom string representation for ExpressionTuple objects for better readability.

```python
from math import log, exp
from numbers import Real
from functools import partial
from operator import add, mul

from unification import var

from etuples.core import etuple, ExpressionTuple

from kanren import run, eq, conde, lall
from kanren.core import success
from kanren.graph import walko, reduceo
from kanren.constraints import isinstanceo

# Just some nice formatting
def etuple_str(self):
    if len(self) > 0:
        return f"{getattr(self[0], '__name__', self[0])}({', '.join(map(str, self[1:]))})"
    else:
        return 'noop'


ExpressionTuple.__str__ = etuple_str
del ExpressionTuple._repr_pretty_
```

--------------------------------

### Querying grandparent relationship

Source: https://github.com/pythological/kanren/blob/main/README.md

Shows how to query for indirect relationships by composing existing relations. This example finds the grandparent of 'Bart' by using two `parent` goals.

```python
>>> from kanren import run, var, parent
>>> grandparent_lv, parent_lv = var(), var()
>>> run(1, grandparent_lv, parent(grandparent_lv, parent_lv),
...                            parent(parent_lv, 'Bart'))
('Abe',)
```

--------------------------------

### Setup: Create Fixed-Point Reduction and Term Walker Relations

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Defines `math_reduceo` as a fixed-point-producing relation using `partial` and `reduceo` for recursive reductions. It also defines `term_walko` for walking term graphs and ensuring outputs are ExpressionTuples.

```python
math_reduceo = partial(reduceo, single_math_reduceo)

term_walko = partial(walko, rator_goal=eq, null_type=ExpressionTuple)
```

--------------------------------

### Expansion Example: Find Graphs Reducing to a Term

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Illustrates finding algebraic graphs that reduce to a specific grounded term, `mul(2, 5)`. It uses `run` to fetch ten such graphs, demonstrating kanren's ability to handle infinite result streams.

```python
expanded_term = var()
reduced_term = etuple(mul, 2, 5)

# Ask for 10 results of `q_lv`
res = run(10, expanded_term, term_walko(math_reduceo, expanded_term, reduced_term))
```

--------------------------------

### Reduction Example: Simplify Algebraic Term

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Demonstrates reducing a complex algebraic term `add(add(3, 3), exp(log(exp(5))))` using the `math_reduceo` and `term_walko` relations. It shows the left-to-right application of reduction rules.

```python
# This is the term we want to reduce
expanded_term = etuple(add, etuple(add, 3, 3), etuple(exp, etuple(log, etuple(exp, 5))))

# Create a logic variable to represent the results we want to compute
reduced_term = var()

# Asking for 0 results means all results
res = run(3, reduced_term, term_walko(math_reduceo, expanded_term, reduced_term))
```

--------------------------------

### Membero goal for set intersection

Source: https://github.com/pythological/kanren/blob/main/README.md

Uses the `membero` goal constructor to find elements common to two collections. This example finds values of `x` present in both `(1, 2, 3)` and `(2, 3, 4)`.

```python
>>> from kanren import run, membero, var
>>> run(0, x, membero(x, (1, 2, 3)),  # x is a member of (1, 2, 3)
...               membero(x, (2, 3, 4)))  # x is a member of (2, 3, 4)
(2, 3)
```

--------------------------------

### Kanren: Restrict object types with isinstanceo

Source: https://github.com/pythological/kanren/blob/main/README.md

Illustrates using the `isinstanceo` constraint to ensure a variable matches a specific type. This example checks if `x` is an instance of `Integral` and a member of a list containing both integers and floats.

```python
from kanren.constraints import neq, isinstanceo
from numbers import Integral

run(0, x,
    isinstanceo(x, Integral),  # `x` must be of type `Integral`
    membero(x, (1.1, 2, 3.2, 4)))

```

--------------------------------

### Constrain Expanded Term to Log Expressions

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Narrows down the search space by using the `conso` goal to specify that the `expanded_term` must be a `log` expression. This refines the results to only include terms starting with the `log` operator.

```python
from kanren.goals import conso

res = run(10, [expanded_term, reduced_term],
          conso(log, var(), expanded_term),
          term_walko(math_reduceo, expanded_term, reduced_term))
```

```python
rjust = max(map(lambda x: len(str(x[0])), res))
print('\n'.join((f'{str(e):>{rjust}} == {str(r)}' for e, r in res)))
```

--------------------------------

### Setup: Define Algebraic Reduction Goal

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Creates a relational goal constructor, `single_math_reduceo`, which implements algebraic rules like x + x == 2 * x and log(exp(x)) == x. It constrains input types to Real and ExpressionTuple using `isinstanceo` and `conde`.

```python
def single_math_reduceo(expanded_term, reduced_term):
    """Construct a goal for some simple math reductions."""
    # Create a logic variable to represent our variable term "x"
    x_lv = var()
    # `conde` is a relational version of Lisp's `cond`/if-else; here, each
    # "branch" pairs the right- and left-hand sides of a replacement rule with
    # the corresponding inputs.
    return lall(
        isinstanceo(x_lv, Real),
        isinstanceo(x_lv, ExpressionTuple),
        conde(
            # add(x, x) == mul(2, x)
            [eq(expanded_term, etuple(add, x_lv, x_lv)),
             eq(reduced_term, etuple(mul, 2, x_lv))],
            # log(exp(x)) == x
            [eq(expanded_term, etuple(log, etuple(exp, x_lv))),
             eq(reduced_term, x_lv)]),
    )
```

--------------------------------

### membero Goal Execution

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Illustrates the execution of the `membero` goal constructor with an unbound variable `x` and a collection, showing the stream of substitutions produced.

```python
>>> for s in membero(x, (1, 2, 3)):
...     print s
{~x: 1}
{~x: 2}
{~x: 3}
```

--------------------------------

### Combining Goals with lall

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Shows how `lall` (logical all) combines two goals, `g1` and `g2`, to produce a stream of substitutions that satisfy both goals.

```python
>>> g1 = membero(x, (1, 2, 3))
>>> g2 = membero(x, (2, 3, 4))
>>> g = lall(g1, g2)
>>> for s in g({}):
...     print s
{~x: 2}
{~x: 3}
```

--------------------------------

### membero Goal with Known Variable

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Demonstrates the `membero` goal when the variable `x` is already known to be `2`, showing that only consistent substitutions are returned.

```python
>>> for s in membero(x, (1, 2, 3), {x: 2}):
...     print s
{~x: 2}
```

--------------------------------

### Combining Goals with lany

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Demonstrates how `lany` (logical any) combines two goals, `g1` and `g2`, to produce a stream of substitutions that satisfy either goal.

```python
>>> g1 = membero(x, (1, 2, 3))
>>> g2 = membero(x, (2, 3, 4))
>>> g = lany(g1, g2)
>>> for s in g({}):
...     print s
{~x: 1}
{~x: 2}
{~x: 3}
{~x: 4}
```

--------------------------------

### Run Function Execution in Pythological Kanren

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Demonstrates the usage of the 'run' function to execute goals and retrieve results. The 'run' function implicitly uses 'lall' and reifies results, abstracting away the need for manual handling of logic variables and substitutions.

```python
>>> x = var()
>>> run(0, x, membero(x, (1, 2, 3)), membero(x, (2, 3, 4)))
(2, 3)
```

--------------------------------

### Lazy Stream Generation

Source: https://github.com/pythological/kanren/blob/main/doc/basic.md

Illustrates the lazy nature of goal streams in miniKanren, showing how a generator object is returned and values can be retrieved one by one using `next()`.

```python
>>> stream = membero(x, (1, 2, 3))({})
>>> stream
<generator object unique at 0x2e13690>
>>> next(stream)
{~x: 1}
```

--------------------------------

### Run kanren tests with Makefile

Source: https://github.com/pythological/kanren/blob/main/README.md

Executes the test suite for the kanren library using the provided Makefile. This command typically runs a series of checks to ensure the library functions correctly.

```bash
make check
```

--------------------------------

### Basic equality goal in kanren

Source: https://github.com/pythological/kanren/blob/main/README.md

Demonstrates a fundamental logic programming concept: finding a value `x` that is equal to 5. It uses `run`, `var`, and `eq` from the kanren library.

```python
>>> from kanren import run, eq, var
>>> x = var()
>>> run(1, x, eq(x, 5))
(5,)
```

--------------------------------

### Expand and Reduce Terms with Kanren

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Initializes logic variables and uses `term_walko` with `math_reduceo` to find expansions and reductions between terms. It demonstrates basic term manipulation and how Kanren applies algebraic relations.

```python
expanded_term = var()
reduced_term = var()

res = run(10, [expanded_term, reduced_term],
          term_walko(math_reduceo, expanded_term, reduced_term))
```

```python
rjust = max(map(lambda x: len(str(x[0])), res))
print('\n'.join((f'{str(e):>{rjust}} == {str(r)}' for e, r in res)))
```

--------------------------------

### Defining and querying parent facts

Source: https://github.com/pythological/kanren/blob/main/README.md

Demonstrates how to define relations and facts in kanren. It sets up a `parent` relation and queries for parents of 'Bart' and children of 'Homer'.

```python
>>> from kanren import Relation, facts, run, var
>>> parent = Relation()
>>> facts(parent, ("Homer", "Bart"),
...               ("Homer", "Lisa"),
...               ("Abe",  "Homer"))

>>> run(1, x, parent(x, "Bart"))
('Homer',)

>>> run(2, x, parent("Homer", x))
('Lisa', 'Bart')
```

--------------------------------

### Clone kanren repository

Source: https://github.com/pythological/kanren/blob/main/README.md

Clones the kanren project from GitHub using Git. This command is typically used to obtain the source code for development or local testing.

```bash
git clone git@github.com:pythological/kanren.git
cd kanren
```

--------------------------------

### Kanren: Restrict unification with neq

Source: https://github.com/pythological/kanren/blob/main/README.md

Demonstrates using the `neq` constraint to exclude specific values from unification. It shows how to ensure a variable `x` is not equal to 1 or 3 while also being a member of a set.

```python
from kanren.constraints import neq, isinstanceo

run(0, x,
    neq(x, 1),  # Not "equal" to 1
    neq(x, 3),  # Not "equal" to 3
    membero(x, (1, 2, 3)))

```

--------------------------------

### Unification with nested structures

Source: https://github.com/pythological/kanren/blob/main/README.md

Illustrates kanren's unification capability with nested data structures (tuples). This query finds the value of `x` that makes the tuple `(1, 2)` equal to `(1, x)`.

```python
>>> from kanren import run, eq, var
>>> run(1, x, eq((1, 2), (1, x)))
(2,)
```

--------------------------------

### Defining a grandparent relation function

Source: https://github.com/pythological/kanren/blob/main/README.md

Defines a custom goal constructor function `grandparent` that encapsulates the logic for finding a grandparent. This promotes reusability of complex relations.

```python
>>> from kanren import run, var, parent, lall
>>> def grandparent(x, z):
...     y = var()
...     return lall(parent(x, y), parent(y, z))

>>> run(1, x, grandparent(x, 'Bart'))
('Abe,')
```

--------------------------------

### Filter Nullary Log Expressions

Source: https://github.com/pythological/kanren/blob/main/doc/graphs.md

Further refines the results by adding a constraint to ensure that the `log` expression's arguments are not empty sequences. This is achieved by using `conso` again to check the structure of the `cdr` term.

```python
exp_term_cdr = var()

res = run(10, [expanded_term, reduced_term],
          conso(log, exp_term_cdr, expanded_term),
          conso(var(), var(), exp_term_cdr),
          term_walko(math_reduceo, expanded_term, reduced_term))
```

```python
rjust = max(map(lambda x: len(str(x[0])), res))
print('\n'.join((f'{str(e):>{rjust}} == {str(r)}' for e, r in res)))
```

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