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Update docs to mention state saving
Browse files- README.md +3 -1
- docs/start.md +3 -1
README.md
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@@ -74,7 +74,7 @@ Most common issues at this stage are solved
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by [tweaking the Julia package server](https://github.com/MilesCranmer/PySR/issues/27).
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to use up-to-date packages.
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#
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Let's create a PySR example. First, let's import
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numpy to generate some test data:
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SymPy format (`sympy_format`), and even JAX and PyTorch format
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(both of which are differentiable).
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There are several other useful features such as denoising (e.g., `denoising=True`),
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feature selection (e.g., `select_k_features=3`).
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For a summary of features and options, see [this docs page](https://pysr.readthedocs.io/en/latest/docs/options/).
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by [tweaking the Julia package server](https://github.com/MilesCranmer/PySR/issues/27).
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to use up-to-date packages.
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+
# Introduction
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Let's create a PySR example. First, let's import
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numpy to generate some test data:
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SymPy format (`sympy_format`), and even JAX and PyTorch format
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(both of which are differentiable).
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+
Note that `PySRRegressor` stores the state of the last search, and will restart from where you left off the next time you call `.fit()`. This will cause problems if significant changes are made to the search parameters (like changing the operators). You can run `model.reset()` to reset the state.
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+
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There are several other useful features such as denoising (e.g., `denoising=True`),
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feature selection (e.g., `select_k_features=3`).
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For a summary of features and options, see [this docs page](https://pysr.readthedocs.io/en/latest/docs/options/).
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docs/start.md
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@@ -19,7 +19,7 @@ Most common issues at this stage are solved
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by [tweaking the Julia package server](https://github.com/MilesCranmer/PySR/issues/27).
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to use up-to-date packages.
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-
#
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Let's create a PySR example. First, let's import
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numpy to generate some test data:
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@@ -89,6 +89,8 @@ This arrow in the `pick` column indicates which equation is currently selected b
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SymPy format (`sympy_format`), and even JAX and PyTorch format
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(both of which are differentiable).
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There are several other useful features such as denoising (e.g., `denoising=True`),
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feature selection (e.g., `select_k_features=3`).
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For a summary of features and options, see [this docs page](https://pysr.readthedocs.io/en/latest/docs/options/).
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by [tweaking the Julia package server](https://github.com/MilesCranmer/PySR/issues/27).
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to use up-to-date packages.
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+
# Introduction
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Let's create a PySR example. First, let's import
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numpy to generate some test data:
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SymPy format (`sympy_format`), and even JAX and PyTorch format
|
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(both of which are differentiable).
|
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|
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+
Note that `PySRRegressor` stores the state of the last search, and will restart from where you left off the next time you call `.fit()`. This will cause problems if significant changes are made to the search parameters (like changing the operators). You can run `model.reset()` to reset the state.
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+
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There are several other useful features such as denoising (e.g., `denoising=True`),
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feature selection (e.g., `select_k_features=3`).
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For a summary of features and options, see [this docs page](https://pysr.readthedocs.io/en/latest/docs/options/).
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