Update CustomFactors.md to more clearly specify types required
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@ -11,7 +11,7 @@ import gtsam
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import numpy as np
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import numpy as np
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from typing import List
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from typing import List
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def error_func(this: gtsam.CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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def error_func(this: gtsam.CustomFactor, v: gtsam.Values, H: List[np.ndarray]) -> np.ndarray:
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...
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...
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```
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```
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@ -21,7 +21,7 @@ def error_func(this: gtsam.CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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If `H` is `None`, it means the current factor evaluation does not need Jacobians. For example, the `error`
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If `H` is `None`, it means the current factor evaluation does not need Jacobians. For example, the `error`
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method on a factor does not need Jacobians, so we don't evaluate them to save CPU. If `H` is not `None`,
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method on a factor does not need Jacobians, so we don't evaluate them to save CPU. If `H` is not `None`,
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each entry of `H` can be assigned a `numpy` array, as the Jacobian for the corresponding variable.
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each entry of `H` can be assigned a (2D) `numpy` array, as the Jacobian for the corresponding variable.
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After defining `error_func`, one can create a `CustomFactor` just like any other factor in GTSAM:
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After defining `error_func`, one can create a `CustomFactor` just like any other factor in GTSAM:
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@ -42,7 +42,7 @@ from typing import List
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expected = Pose2(2, 2, np.pi / 2)
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expected = Pose2(2, 2, np.pi / 2)
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]) -> np.ndarray:
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"""
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"""
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Error function that mimics a BetweenFactor
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Error function that mimics a BetweenFactor
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:param this: reference to the current CustomFactor being evaluated
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:param this: reference to the current CustomFactor being evaluated
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