Add a detailed example to the README

README.md

@@ -152,6 +152,83 @@ For examples of these and other features, see the [examples page](https://astroa
 For a detailed look at more options, see the [options page](https://astroautomata.com/PySR/#/options).
 You can also see the full API at [this page](https://astroautomata.com/PySR/#/api).
 
+## Detailed Example
+
+The following code makes use of as many PySR features as possible.
+Note that in practice you may not want to run with all of these, but
+it shows the types of things you can do.
+
+```python
+model = PySRRegressor(
+    procs=4,
+    populations=8,
+    # ^ 2 populations per core, so one is always running.
+    population_size=50,
+    # ^ Slightly larger populations, for greater diversity.
+    ncyclesperiteration=500, 
+    # ^ Generations between migrations.
+    niterations=10000000,  # Run forever
+    early_stop_condition=(
+        "stop_if(loss, complexity) = loss < 1e-6 && complexity < 10"
+        # Stop early if we find a good and simple equation
+    ),
+    timeout_in_seconds=60 * 60 * 24,
+    # ^ Alternatively, stop after 24 hours have passed.
+    maxsize=50,
+    # ^ Allow greater complexity.
+    maxdepth=10,
+    # ^ But, avoid deep nesting.
+    binary_operators=["*", "+", "-", "/"],
+    unary_operators=["square", "cube", "exp", "cos2(x)=cos(x)^2"],
+    constraints={
+        "/": (-1, 9),
+        "square": 9,
+        "cube": 9,
+        "exp": 9,
+    },
+    # ^ Limit the complexity within each argument.
+    # "inv": (-1, 9) states that the numerator has no constraint,
+    # but the denominator has a max complexity of 9.
+    # "exp": 9 simply states that `exp` can only have
+    # an expression of complexity 9 as input.
+    nested_constraints={
+        "square": {"square": 1, "cube": 1, "exp": 0},
+        "cube": {"square": 1, "cube": 1, "exp": 0},
+        "exp": {"square": 1, "cube": 1, "exp": 0},
+    },
+    # ^ Nesting constraints on operators. For example,
+    # "square(exp(x))" is not allowed, since "square": {"exp": 0}.
+    complexity_of_operators={"/": 2, "exp": 3},
+    # ^ Custom complexity of particular operators.
+    complexity_of_constants=2,
+    # ^ Punish constants more than variables
+    select_k_features=4,
+    # ^ Train on only the 4 most important features
+    progress=True,
+    # ^ Can set to false if printing to a file.
+    weight_randomize=0.1,
+    # ^ Randomize the tree much more frequently
+    cluster_manager=None,
+    # ^ Can be set to, e.g., "slurm", to run a slurm
+    # cluster. Just launch one script from the head node.
+    precision=64,
+    # ^ Higher precision calculations.
+    warm_start=True,
+    # ^ Start from where left off.
+    julia_project=None,
+    # ^ Can set to the path of a folder containing the
+    # "SymbolicRegression.jl" repo, for custom modifications.
+    update=False,
+    # ^ Don't update Julia packages
+    extra_sympy_mappings={"cos2": lambda x: sympy.cos(x)^2},
+    extra_torch_mappings={sympy.cos: torch.cos},
+    # ^ Not needed as cos already defined, but this
+    # is how you define custom torch operators.
+    extra_jax_mappings={sympy.cos: "jnp.cos"},
+    # ^ For JAX, one passes a string.
+)
+```
+
 
 # Docker