Preparation:
This has a hasktorch integration that requires some setup. Copy the get-deps.sh file into the dataframe home directory. This will download and link some pytorch files required to run the examples.
After this is done you'll need to run ./set_hasktorch_env to put the hasktorch libraries in your LD_LIBRARY_PATH.
Running:
cabal run california_housing.
Expected output:
Training linear regression model...
Iteration: 10000 | Loss: Tensor Float [] 5.0225e9
Iteration: 20000 | Loss: Tensor Float [] 4.9093e9
Iteration: 30000 | Loss: Tensor Float [] 4.8576e9
Iteration: 40000 | Loss: Tensor Float [] 4.8333e9
Iteration: 50000 | Loss: Tensor Float [] 4.8217e9
Iteration: 60000 | Loss: Tensor Float [] 4.8160e9
Iteration: 70000 | Loss: Tensor Float [] 4.8130e9
Iteration: 80000 | Loss: Tensor Float [] 4.8114e9
Iteration: 90000 | Loss: Tensor Float [] 4.8105e9
Iteration: 100000 | Loss: Tensor Float [] 4.8099e9
--------------------------------------------------
index | median_house_value | predicted_house_value
------|--------------------|----------------------
Int | Double | Float
------|--------------------|----------------------
0 | 452600.0 | 414079.94
1 | 358500.0 | 423011.94
2 | 352100.0 | 383239.06
3 | 341300.0 | 324928.94
4 | 342200.0 | 256934.23
5 | 269700.0 | 264944.84
6 | 299200.0 | 259094.13
7 | 241400.0 | 257224.55
8 | 226700.0 | 201753.69
9 | 261100.0 | 268698.7