Upload PAM

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+PAMdata/processed_data/AR_8p_8c.mat filter=lfs diff=lfs merge=lfs -text

PAMdata/process_scripts/Generate_splitID.py

@@ -0,0 +1,30 @@
+import numpy as np
+
+# arr_outcomes = np.load('../processed_data/arr_outcomes.npy', allow_pickle=True)
+
+# split randomization over folds
+"""Use 9:1:1 split"""
+p_train = 0.80
+p_val   = 0.10
+p_test  = 0.10
+
+
+n =  11988  # original 12000 patients, remove 12 outliers
+n_train = round(n*p_train)
+n_val   = round(n*p_val)
+n_test  = n - (n_train+n_val)
+print(n_train, n_val, n_test)
+Nsplits = 5
+for j in range(Nsplits):
+    p = np.random.permutation(n)
+    idx_train = p[:n_train]
+    idx_val   = p[n_train:n_train+n_val]
+    idx_test  = p[n_train+n_val:]
+    np.save('../splits/phy12_split'+str(j+1)+'.npy', (idx_train, idx_val, idx_test))
+
+    # np.save('../splits/phy12_split_subset'+str(j+1)+'.npy', (idx_train, idx_val, idx_test))
+print('split IDs saved')
+
+# # check first split
+# idx_train,idx_val,idx_test = np.load('../splits/phy12_split1.npy', allow_pickle=True)
+# print(len(idx_train), len(idx_val), len(idx_test))

PAMdata/processed_data/AR_8p_8c.mat

@@ -0,0 +1,3 @@
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+size 498862643

PAMdata/processed_data/PTdict_list.npy

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+size 435172928

PAMdata/processed_data/arr_outcomes.npy

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+oid sha256:fe01a1e479b58521f1a16bf8d310a5b3fcf5d62ab24d1cad3e863c7c06324682
+size 42792

PAMdata/processed_data/readme

@@ -0,0 +1,5 @@
+AR_6p_8c.mat, AR_8p_8c.mat
+
+# # PAMAP2 120,000 samples for 6 or 8 subjects. 20,000 samples for each sub. 51 features, 8 activities(0-7)
+feature = sc.loadmat("/home/xiangzhang/matlabwork/AR_6p_8c.mat")
+all = feature['AR_6p_8c']

PAMdata/splits/PAMAP2_split_1.npy

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+size 21781

PAMdata/splits/PAMAP2_split_2.npy

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+size 21781

PAMdata/splits/PAMAP2_split_3.npy

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+size 21781

PAMdata/splits/PAMAP2_split_4.npy

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+size 21781

PAMdata/splits/PAMAP2_split_5.npy

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