import torch
import pandas as pd
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
from output_writer import OutputWriter
ow = OutputWriter(5)
ow.write_to_csv(verbose=True)
# Create Tensors to hold dependent/independent variable data
train_csv = ow.get_cached_csv("train")
train_ind = pd.read_csv(train_csv)[["f1", "f2", "f3", "f4", "f5"]]
train_dep = pd.read_csv(train_csv)[["phone_class_index"]]
x = torch.from_numpy(train_ind.values).float()
y = torch.from_numpy(train_dep.values).long()
print(x)
print(y)
# Create a TensorDataset and DataLoader to provide the model with batches of data
train_ds = TensorDataset(x, y)
train_dl = DataLoader(train_ds, batch_size=32)
##### Set model layer dimensions
### D_in is the input dimension (5, one for each estimated formant)
D_in = x.shape[1]
### H is the hidden layer dimension
H = 16
### C is the number of final categories (there are 14 monophthongs)
C = 14
