added finalise

.gitignore

@@ -1,4 +1,5 @@
 # Byte-compiled / optimized / DLL files
+*.pkg
 *.mp4
 *.xz
 *.json

app.py

@@ -13,7 +13,7 @@ from funcs.dataloader import BaseDataset2, read_json_files
 
 DEVICE = torch.device("cpu")
 reducer10d = PHATEAE(epochs=30, n_components=10, lr=.0001, batch_size=128, t='auto', knn=8, relax=True, metric='euclidean')
-reducer10d.load('models/r10d_2.pth')
+reducer10d.load('models/r10d.pth')
 
 cluster_som = ClusterSOM()
 cluster_som.load("models/cluster_som2.pkl")

funcs/dataloader.py

@@ -1,6 +1,7 @@
 import glob, json, os
 import torch
 import warnings
+import numpy as np
 
 from torch.utils.data import Dataset
 

funcs/som.py

@@ -435,7 +435,10 @@ class ClusterSOM:
         if len(self.som_models) == 0:
             raise ValueError("SOM models not trained yet.")
         
-        prediction = self.predict([data[int(slice_select)-1]])[0]
+        try:
+            prediction = self.predict([data[int(slice_select)-1]])[0]
+        except:
+            prediction = self.predict([data[int(slice_select)-2]])[0]
 
         fig, axes = plt.subplots(1, len(self.som_models), figsize=(20, 5), sharex=True, sharey=True)
         fig.suptitle(f"Activation map for SOM {prediction[0]}, node {prediction[1]}", fontsize=16)

models/r10d.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad5529c863ea3fcaf1525cf59b9352dfe42ff4568ed459ac978baaf818a8ad7d
+size 13100223

test.py

@@ -0,0 +1,47 @@
+import torch
+import gradio as gr
+import json
+import os
+import matplotlib.pyplot as plt
+
+from phate import PHATEAE
+from funcs.som import ClusterSOM
+from funcs.tools import numpy_to_native
+
+from funcs.processor import process_data
+from funcs.plot_func import plot_sensor_data_from_json
+from funcs.dataloader import BaseDataset2, read_json_files
+
+DEVICE = torch.device("cpu")
+reducer10d = PHATEAE(epochs=30, n_components=10, lr=.0001, batch_size=128, t='auto', knn=8, relax=True, metric='euclidean')
+reducer10d.load('models/r10d_2.pth')
+
+cluster_som = ClusterSOM()
+cluster_som.load("models/cluster_som2.pkl")
+
+# ml inference
+def get_som_mp4(file, slice_select, reducer=reducer10d, cluster=cluster_som):
+
+    try:
+        train_x, train_y  = read_json_files(file)
+    except:
+        train_x, train_y  = read_json_files(file.name)
+
+    # Convert tensors to numpy arrays if necessary
+    if isinstance(train_x, torch.Tensor):
+        train_x = train_x.numpy()
+    if isinstance(train_y, torch.Tensor):
+        train_y = train_y.numpy()
+
+    # load the time series slices of the data 4*3*2*64 (feeds+axis*sensor*samples) + 5 for time diff
+    data = BaseDataset2(train_x.reshape(len(train_x), -1) / 32768, train_y)
+
+    #compute the 10 dimensional embeding vector
+    embedding10d = reducer.transform(data)
+
+    # prediction = cluster_som.predict(embedding10d)
+    fig = cluster.plot_activation_v2(embedding10d, slice_select)
+    plt.savefig('test.png')
+    return fig
+
+get_som_mp4('Data-JSON/Dressage/Tempi/Trab/Arbeitstrab/20210906-093200-Don-Arbeitstrab.json', 1)