python_api | Python client for RiskIQ API services | REST library

It seems that XGBoost includes a global bias in the model, and that this is fixed at 0.5 rather than being calculated based on the input data. This has been raised as an issue in the XGBoost GitHub repository (see https://github.com/dmlc/xgboost/issues/799). The corresponding hyperparameter is base_score, if you set it equal to zero your model will predict zero as expected.

The documentation seems poor as you say, searching through the XGBoost source code on Github gives some tests that show these options:

• int
• float
• q: quantitative
• i: indicator

While it is a bit difficult to figure out what these mean, some other sites list some additional information:

• i: "i means this feature is binary indicator feature"
• q: "means this feature is a quantitative value, such as age, time, can be missing"
• int: "means this feature is integer value (when int is hinted, the decision boundary will be integer)"

Link: another StackOverflow post that mentions the q and i types.

In XGBoosts core.py code you can also find a comment on types:

Concerning NetworKit: an algorithm to compute an exact maximum matching is not yet provided, but you can use networkit.matching.PathGrowingMatcher, which implements the linear time 1/2-approximation algorithm for maximum matching by Drake and Hougardy [1]. You can use it as follows:

Looking at the link you shared, I see the OBB object has the following properties: center, extent and R. If you can access them then you can get position and orientation. Center is a point (x,y,z), extent are three lengths in x, y and z direction and R is a rotation matrix. Columns of R are three orthogonal unit-vectors pointing on rotated x, y and z directions.

I think you are interested in orientation, so R is the orientation matrix. You can convert it to quaternion using the matrix-to-quaternion method on this page: https://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/

Well, if you want the exhaustive list, you could look at the source at GitHub. Searching with keywords on github gives good insignt.

search for 'rand' - 15 results

search for 'seed' and python filter - 20 results

Actually there are two ways of having meshlab functionality in python:

1. The first is MeshLabXML (https://github.com/3DLIRIOUS/MeshLabXML ) a third party, is a Python scripting interface to meshlab scripting interface
2. the second is PyMeshLab (https://github.com/cnr-isti-vclab/PyMeshLab ) an ongoing effort done by the MeshLab authors, (currently in alpha stage) to have a direct Python bindings to all the meshlab filters

I found a solution, maybe it will be useful for someone else. You can see details with code examples here: https://github.com/mlflow/mlflow/issues/3592

Because you use single quotes twice you get: print(f'{ newline }### Initializing project with the following tasks: { ' instead of

After searching around, I found this issue related to my problem above. Actually, all my metrics just logged once each training (instead of each epoch as my intuitive thought). The reason is I didn't specify the every_n_iter parameter in mlflow.tensorflow.autolog(), which indicates how many 'iterations' must pass before MLflow logs metric executed (see the docs). So, changing my code to:

mlflow.tensorflow.autolog(every_n_iter=1)

fixed the problem.

P/s: Remember that in TF 2.x, an 'iteration' is an epoch (in TF 1.x it's a batch).