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fn test_tree_ensemble_classifier_binary_none() {
let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(POST_TRANSFORM::NONE);
let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(classifier, X);
assert(*labels[0] == 1, 'labels[0]');
assert(labels.len() == 1, 'len(labels)');
assert(
relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 0, sign: false }) == true,
'score[0, 0]'
);
assert(
relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 65536, sign: false }) == true,
'score[0, 1]'
);
} |
fn test_tree_ensemble_classifier_binary_logistic() {
let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(
POST_TRANSFORM::LOGISTIC
);
let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(classifier, X);
assert(*labels[0] == 1, 'labels[0]');
assert(labels.len() == 1, 'len(labels)');
assert(
relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 17625, sign: false }) == true,
'score[0, 0]'
);
assert(
relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 47910, sign: false }) == true,
'score[0, 1]'
);
} |
fn test_tree_ensemble_classifier_binary_softmax() {
let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(POST_TRANSFORM::SOFTMAX);
let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(classifier, X);
assert(*labels[0] == 1, 'labels[0]');
assert(labels.len() == 1, 'len(labels)');
assert(
relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 7812, sign: false }) == true,
'score[0, 0]'
);
assert(
relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 57723, sign: false }) == true,
'score[0, 1]'
);
} |
fn test_tree_ensemble_classifier_binary_softmax_zero() {
let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(
POST_TRANSFORM::SOFTMAXZERO
);
let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(classifier, X);
assert(*labels[0] == 1, 'labels[0]');
assert(labels.len() == 1, 'len(labels)');
assert(
relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 7812, sign: false }) == true,
'score[0, 0]'
);
assert(
relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 57723, sign: false }) == true,
'score[0, 1]'
);
}
fn tree_ensemble_classifier_helper(
post_transform: POST_TRANSFORM
) -> (TreeEnsembleClassifier<FP16x16>, Tensor<FP16x16>) {
let class_ids: Span<usize> = array![0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]
.span();
let class_nodeids: Span<usize> = array![2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 3, 3, 3, 4, 4, 4]
.span();
let class_treeids: Span<usize> = array![0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
.span();
let class_weights: Span<FP16x16> = array![
FP16x16 { mag: 30583, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 2185, sign: false },
FP16x16 { mag: 13107, sign: false },
FP16x16 { mag: 15729, sign: false },
FP16x16 { mag: 3932, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 29491, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 3277, sign: false },
FP16x16 { mag: 6746, sign: false },
FP16x16 { mag: 12529, sign: false },
FP16x16 { mag: 13493, sign: false },
]
.span();
let classlabels: Span<usize> = array![0, 1, 2].span(); |
let nodes_falsenodeids: Span<usize> = array![4, 3, 0, 0, 0, 2, 0, 4, 0, 0].span();
let nodes_featureids: Span<usize> = array![1, 0, 0, 0, 0, 1, 0, 0, 0, 0].span();
let nodes_missing_value_tracks_true: Span<usize> = array![0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span();
let nodes_modes: Span<NODE_MODES> = array![
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
]
.span();
let nodes_nodeids: Span<usize> = array![0, 1, 2, 3, 4, 0, 1, 2, 3, 4].span();
let nodes_treeids: Span<usize> = array![0, 0, 0, 0, 0, 1, 1, 1, 1, 1].span();
let nodes_truenodeids: Span<usize> = array![1, 2, 0, 0, 0, 1, 0, 3, 0, 0].span();
let nodes_values: Span<FP16x16> = array![
FP16x16 { mag: 81892, sign: false },
FP16x16 { mag: 19992, sign: true },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 110300, sign: true },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 44245, sign: true },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
]
.span();
let tree_ids: Span<usize> = array![0, 1].span();
let mut root_index: Felt252Dict<usize> = Default::default();
root_index.insert(0, 0);
root_index.insert(1, 5);
let mut node_index: Felt252Dict<usize> = Default::default();
node_index
.insert(2089986280348253421170679821480865132823066470938446095505822317253594081284, 0);
node_index
.insert(2001140082530619239661729809084578298299223810202097622761632384561112390979, 1);
node_index
.insert(2592670241084192212354027440049085852792506518781954896144296316131790403900, 2);
node_index
.insert(296059127137682937835656780361854867203486734512372717862886 |
9426548453833420, 3);
node_index
.insert(458933264452572171106695256465341160654132084710250671055261382009315664425, 4);
node_index
.insert(1089549915800264549621536909767699778745926517555586332772759280702396009108, 5);
node_index
.insert(1321142004022994845681377299801403567378503530250467610343381590909832171180, 6);
node_index
.insert(2592987851775965742543459319508348457290966253241455514226127639100457844774, 7);
node_index
.insert(2492755623019086109032247218615964389726368532160653497039005814484393419348, 8);
node_index
.insert(1323616023845704258113538348000047149470450086307731200728039607710316625916, 9);
let atts = TreeEnsembleAttributes {
nodes_falsenodeids,
nodes_featureids,
nodes_missing_value_tracks_true,
nodes_modes,
nodes_nodeids,
nodes_treeids,
nodes_truenodeids,
nodes_values
};
let mut ensemble: TreeEnsemble<FP16x16> = TreeEnsemble {
atts, tree_ids, root_index, node_index
};
let base_values: Option<Span<FP16x16>> = Option::None;
let mut classifier: TreeEnsembleClassifier<FP16x16> = TreeEnsembleClassifier {
ensemble,
class_ids,
class_nodeids,
class_treeids,
class_weights,
classlabels,
base_values,
post_transform
};
let mut X: Tensor<FP16x16> = TensorTrait::new(
array![3, 3].span(),
array![
FP16x16 { mag: 65536, sign: true },
FP16x16 { mag: 52429, sign: true },
FP16x16 { mag: 39322, sign: true },
FP16x16 { mag: 26214, sign: true },
FP16x16 { mag: 13107, sign: true },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 13107, sign: false },
FP16x16 { mag: 26214, sign: false },
FP16x16 { mag: 39322, sign: false },
]
.span()
);
(classifier, X)
}
fn tree_ensemble_classifier_binary_class_helper(
post_tra |
nsform: POST_TRANSFORM
) -> (TreeEnsembleClassifier<FP16x16>, Tensor<FP16x16>) {
let class_ids: Span<usize> = array![
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
]
.span();
let class_nodeids: Span<usize> = array![
4,
5,
7,
10,
12,
13,
15,
17,
19,
20,
24,
26,
29,
31,
32,
33,
37,
38,
39,
40,
46,
49,
50,
52,
56,
57,
58,
59,
62,
64,
66,
67,
68,
73,
74,
75,
76,
81,
82,
83,
84,
88,
89,
91,
93,
94,
95,
98,
99,
101,
104,
106,
107,
108,
112,
113,
114,
115,
119,
121,
124,
125,
127,
128,
130,
131,
138,
140,
141,
142,
143,
148,
149,
150,
151,
152,
153,
154
]
.span();
let class_tre |
eids: Span<usize> = array![
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
]
.span();
let class_weights: Span<FP16x16> = array![
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 |
{ mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 43690, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16 |
x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false }
]
.span();
let classlabels: Span<usize> = array![0, 1].span();
let nodes_falsenodeids: Span<usize> = array![
116,
21,
6,
5,
0,
0,
8,
0,
14,
11,
0,
13,
0,
0,
16,
0,
18,
0,
20,
0,
0,
41,
34,
25,
0,
27,
0,
33,
30,
0,
32,
0,
0,
0,
40,
39,
38,
0,
0,
0,
0,
109,
96,
69,
60,
47,
0,
51,
50,
0,
0,
53,
0,
59,
58,
57,
0,
0,
0,
0,
68,
63,
0,
65,
0,
67,
0,
0,
0,
77,
76,
75,
74,
0,
0,
0,
0,
85,
84,
83,
82,
0,
0,
0,
0,
95,
90,
89,
0,
0,
92,
0,
94,
0,
0,
0,
100,
99,
0,
0,
102,
0,
108,
105,
0,
107,
0,
0,
0,
115,
114,
113,
0,
0,
0,
0,
132,
129,
120,
0,
122,
0,
126,
125,
0,
0,
128,
0,
0,
131,
0,
0,
154,
153,
144,
143,
142,
139,
0,
141,
0,
0,
0,
0,
152,
151,
15 |
0,
149,
0,
0,
0,
0,
0,
0,
0
]
.span();
let nodes_featureids: Span<usize> = array![
3,
2,
4,
8,
0,
0,
1,
0,
2,
7,
0,
0,
0,
0,
7,
0,
0,
0,
6,
0,
0,
8,
0,
2,
0,
7,
0,
7,
2,
0,
2,
0,
0,
0,
2,
6,
7,
0,
0,
0,
0,
7,
7,
0,
7,
1,
0,
0,
2,
0,
0,
2,
0,
2,
2,
6,
0,
0,
0,
0,
2,
0,
0,
1,
0,
6,
0,
0,
0,
0,
2,
6,
7,
0,
0,
0,
0,
6,
7,
2,
0,
0,
0,
0,
0,
2,
2,
7,
0,
0,
2,
0,
0,
0,
0,
0,
6,
1,
0,
0,
4,
0,
2,
2,
0,
0,
0,
0,
0,
0,
1,
2,
0,
0,
0,
0,
6,
0,
7,
0,
0,
0,
1,
3,
0,
0,
2,
0,
0,
8,
0,
0,
2,
2,
2,
4,
7,
3,
0,
1,
0,
0,
0,
0,
4,
3,
7,
8,
0,
0,
0,
0,
0,
0,
0
]
.span();
let nodes_missing_value_tracks_true: Span<usize> = array![
0,
0,
0,
0,
0,
0,
0,
0, |
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
]
.span();
let nodes_modes: Span<NODE_MODES> = array![
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ, |
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES:: |
LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF |
,
NODE_MODES::LEAF
]
.span();
let nodes_nodeids: Span<usize> = array![
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
66,
67,
68,
69,
70,
71,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
83,
84,
85,
86,
87,
88,
89,
90,
91,
92,
93,
94,
95,
96,
97,
98,
99,
100,
101,
102,
103,
104,
105,
106,
107,
108,
109,
110,
111,
112,
113,
114,
115,
116,
117,
118,
119,
120,
121,
122,
123,
124,
125,
126,
127,
128,
129,
130,
131,
132,
133,
134,
135,
136,
137,
138,
139,
140,
141,
142,
143,
144,
145,
146,
147,
148,
149,
150,
151,
152,
153,
154
]
.span();
let nodes_treeids: Spa |
n<usize> = array![
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
]
.span();
let nodes_truenodeids: Span<usize> = array![
1,
2,
3,
4,
0,
0,
7,
0,
9,
10,
0,
12,
0,
0,
15,
0,
17,
0,
19,
0,
0,
22, |
23,
24,
0,
26,
0,
28,
29,
0,
31,
0,
0,
0,
35,
36,
37,
0,
0,
0,
0,
42,
43,
44,
45,
46,
0,
48,
49,
0,
0,
52,
0,
54,
55,
56,
0,
0,
0,
0,
61,
62,
0,
64,
0,
66,
0,
0,
0,
70,
71,
72,
73,
0,
0,
0,
0,
78,
79,
80,
81,
0,
0,
0,
0,
86,
87,
88,
0,
0,
91,
0,
93,
0,
0,
0,
97,
98,
0,
0,
101,
0,
103,
104,
0,
106,
0,
0,
0,
110,
111,
112,
0,
0,
0,
0,
117,
118,
119,
0,
121,
0,
123,
124,
0,
0,
127,
0,
0,
130,
0,
0,
133,
134,
135,
136,
137,
138,
0,
140,
0,
0,
0,
0,
145,
146,
147,
148,
0,
0,
0,
0,
0,
0,
0
]
.span();
let nodes_values: Span<FP16x16> = array![
FP16x16 { mag: 4096, sign: false },
FP16x16 { mag: 22937, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 16384, sign: false },
FP16x16 { mag: 57344, sign: fal |
se },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 19660, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 29491, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 24576, sign: false },
FP16x16 { mag: 42598, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 62259, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 62259, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 40960, sign: false },
FP16x16 { mag: 24576, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 19660, sign: false },
FP16x16 { mag: 45875, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 29491, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 42598, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { |
mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 36044, sign: false },
FP16x16 { mag: 19660, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 45875, sign: false },
FP16x16 { mag: 29491, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 36044, sign: false },
FP16x16 { mag: 58982, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 58982, sign: false },
FP16x16 { mag: 29491, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 45875, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 58982, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 42598, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x |
16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 45875, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 29491, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 45875, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 36044, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 58982, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 36044, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 16384, sign: false },
FP16x16 { mag: 20480, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 49152, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 8192, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false }, |
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false }
]
.span();
let base_values: Option<Span<FP16x16>> = Option::None;
let tree_ids: Span<usize> = array![0].span();
let mut root_index: Felt252Dict<usize> = Default::default();
root_index.insert(0, 0);
let mut node_index: Felt252Dict<usize> = Default::default();
node_index
.insert(2089986280348253421170679821480865132823066470938446095505822317253594081284, 0);
node_index
.insert(2001140082530619239661729809084578298299223810202097622761632384561112390979, 1);
node_index
.insert(2592670241084192212354027440049085852792506518781954896144296316131790403900, 2);
node_index
.insert(2960591271376829378356567803618548672034867345123727178628869426548453833420, 3);
node_index
.insert(458933264452572171106695256465341160654132084710250671055261382009315664425, 4);
node_index
.insert(3344223123784052057366048933846905716067140384361791026153972616805110454637, 5);
node_index
.insert(658476905110174425295568215706634733332002869979287079110965040248935650599, 6);
node_index
.insert(2836212335642438363012490794290757623813171043187182819737087983331902926990, 7);
node_index
.insert(3496601277869056110810900082189273917786762659443522403285387602989271154262, 8);
node_index
.insert(1249294489531540970169611621067106471309281870082955806338234725206665112557, 9);
node_index
.insert(2161697998033672097816961828039488190903838124365465380011173778905747857792, 10);
node_index
.insert(1129815197211541481934112806673325772687763881719835256646064516195041515616, 11);
node_index
.insert(2592593088135949192377729543480191336537305484235681164569491942155715064163, 12);
node_index
.insert(578223957014284909949571568465953382377214912750427143720957054706073492593, 13);
node_index
.insert(1645617302026197421098102802983206579163506957138012501615708926 |
120228167528, 14);
node_index
.insert(2809438816810155970395166036110536928593305127049404137239671320081144123490, 15);
node_index
.insert(2496308528011391755709310159103918074725328650411689040761791240500618770096, 16);
node_index
.insert(2003594778587446957576114348312422277631766150749194167061999666337236425714, 17);
node_index
.insert(2215681478480673835576618830034726157921200517935329010004363713426342305479, 18);
node_index
.insert(3185925835074464079989752015681272863271067691852543168049845807561733691707, 19);
node_index
.insert(1207265836470221457484062512091666004839070622130697586496866096347024057755, 20);
node_index
.insert(1870230949202979679764944800468118671928852128047695497376875566624821494262, 21);
node_index
.insert(618060852536781954395603948693216564334274573299243914053414488061601327758, 22);
node_index
.insert(232760707548494477255512699093366059519467428168757247456690480397246371463, 23);
node_index
.insert(1617386247965480308136742715422077429967341022950306068917456849194882895900, 24);
node_index
.insert(654822874782506608656472905579051041410086644071534146326024101025575400153, 25);
node_index
.insert(525638101901638132526332140778087078272370083489998903571807698910013602668, 26);
node_index
.insert(3091640181556387972179279087539287892670640556085669903494551919685982442095, 27);
node_index
.insert(1425411460578159050163131982087304445715005458700346341117759372943452688022, 28);
node_index
.insert(1722933265299553894839124723076027659619615015638971980461286818493531809034, 29);
node_index
.insert(3325117385742592388671007840076299062858228097051060057749225651290693960897, 30);
node_index
.insert(1869273998012404873272699831805499731567895666937555882116307079956228100456, 31);
node_index
.insert(2572623952349108258790339518014238358356302709678466644131545945 |
20703929530, 32);
node_index
.insert(2891500475385583315757684141371327604925143655360011721762142660942782195029, 33);
node_index
.insert(1257459981124043271342269816753070228024611695909553991758648317372015085782, 34);
node_index
.insert(3573101724490615587655146760489247477770015274618159524231872921394794809579, 35);
node_index
.insert(2951401777594449283985541406642940553317465718696638438535370997641527993378, 36);
node_index
.insert(2436860863451320452900512817385686838091627966322316039332239784330434600829, 37);
node_index
.insert(3257977356974702770994741663931928753019715185508521958836925918758890988390, 38);
node_index
.insert(2741853283805093821434776875305720302351684616683152528499335618682018880592, 39);
node_index
.insert(514567459251558911686762246500770717674979116530125263461114578537254680672, 40);
node_index
.insert(2119374930171040799805795099091470687208894498354655018353474015395489390434, 41);
node_index
.insert(3338470191188327918255138125570464269857839379813971679216902484398948556964, 42);
node_index
.insert(2892272281879752543368066497063301979597320550780387266511926397533716561161, 43);
node_index
.insert(2855312300216814846973137837923466865382642814675378398541743368270404441020, 44);
node_index
.insert(3483159989811162048659069774034779954374540681397531094699912464364012442948, 45);
node_index
.insert(2987290998320166766043911843685118029159841654368226419198314196237253901671, 46);
node_index
.insert(2925128850088180758852255336587985612621894021863350117875677692518888637440, 47);
node_index
.insert(2816470536741550741568042622139415760794090671576940833850781679568928363263, 48);
node_index
.insert(117504025904364990582663097556885493352655695615775952177872159762046032741, 49);
node_index
.insert(214322841029414923935490161279754016700306696691013227806062624 |
1695943498248, 50);
node_index
.insert(419311759585766455354017006957403420381614228026953716552023555428752798694, 51);
node_index
.insert(3050064038480880151202753004776919876287903442365303272956696507808448797287, 52);
node_index
.insert(1385347512411195789080079656286641766866442255046855963092069449745407366357, 53);
node_index
.insert(3070310993421490198115289431281422702215620142859327949152517372324361472619, 54);
node_index
.insert(2913742884576958969164113782587195202828846527657900496424141449477472273564, 55);
node_index
.insert(2093568472535973986606438755824580633177115509557931302974988564932601955239, 56);
node_index
.insert(3560543329106347446823281318204312198881533222464682017397248462954529220234, 57);
node_index
.insert(2258329791422139736262782239641765930569031761627249090322755566443202104242, 58);
node_index
.insert(780147230530856456622774510057100334628735431063744145772648079601317149643, 59);
node_index
.insert(2316329094783634722527635915976455864728431870713378530935487247638854220445, 60);
node_index
.insert(595942459003356191117553450912822964169058193996898486073017533717706655996, 61);
node_index
.insert(468061318535033931711585815055033307297228787991312757359512916260570188285, 62);
node_index
.insert(2052204235688624923559873131063770183910134013049526186717275231865702195614, 63);
node_index
.insert(1699955311620840869165542755053722387608345658646185648087789689690825797785, 64);
node_index
.insert(3374282522812564185678772854203408947562394461702303390331208821006329361123, 65);
node_index
.insert(2973169188135795465401576355486514117723575153845438471619715618155257254587, 66);
node_index
.insert(1933845760462748501896196912926633344425020928596291295340561855718789280752, 67);
node_index
.insert(140020637430883995967670867621733456958073805204979876655684851 |
6900888958934, 68);
node_index
.insert(1440488595273849761788031183901254714714513692476890759699232177835922420051, 69);
node_index
.insert(1765607197782429306903827944694032984087223086461400721152786273443512274576, 70);
node_index
.insert(1081728107764482028110815183657783965582618309560569428049406599883158895762, 71);
node_index
.insert(2062101824085365476835789898002802715794623271831111740147610520210138854237, 72);
node_index
.insert(2074740322618091900768870458741540994849904300182495465356314088191301853065, 73);
node_index
.insert(3258451235037745323160669027918885172565773098482160366154412360890640013860, 74);
node_index
.insert(525053653813541387331907730505904505067816165493211829943994988775279102044, 75);
node_index
.insert(1899573658331441767985549642643113663505618738939032010935036740376062596854, 76);
node_index
.insert(350484224543766923071449868701665032398970313961410080649918872017849315812, 77);
node_index
.insert(1950842492180490337143378914485176805944281696420768035114335939818602766139, 78);
node_index
.insert(1404824782481446239312837894341789608778585592445990662138109764117920511709, 79);
node_index
.insert(362836422984951199752185473435750713386745407518736982952373985921347236081, 80);
node_index
.insert(946623025367211063265176586824604502073515634531788667777364911179858705558, 81);
node_index
.insert(2633163324000277496191816132521100721217797223993064604664039067710591734562, 82);
node_index
.insert(1801986104078933931671502775029170829560335045042499367678597186639133610708, 83);
node_index
.insert(1420697278439090953165809531316265389371075037014378922361911811337560296928, 84);
node_index
.insert(2818913779862691152404893285048164649343019708946413114150419613972391643833, 85);
node_index
.insert(211799543601365272849784088548054572983303091348684811809375872 |
6746902541269, 86);
node_index
.insert(127751852951361188238686395231851222850913859197429858579312845246901369178, 87);
node_index
.insert(2698811633001158191033663638617437313508153976714307643233173949778419312517, 88);
node_index
.insert(658388282521842455588914251287531837029259203197178137902217792556456503561, 89);
node_index
.insert(1181527093320872098458354979612125149419384756607076935731557552577945926179, 90);
node_index
.insert(749436134732178646256740138670151907037714564259781780243747781475007506978, 91);
node_index
.insert(139527053159256821789882596124320673637475746672994443968014105962305658551, 92);
node_index
.insert(2256264752321707533173578319742847366660740117899562657584919346001438808295, 93);
node_index
.insert(1471349294215639651865069312281269029496180149092207674923855978537861742949, 94);
node_index
.insert(1599527610774916650758786135513735847459194869088601099692148267264507139422, 95);
node_index
.insert(1348925567371118538973078195838174941892601233016661969987842843098656775084, 96);
node_index
.insert(3255130909854220350850821724488067913492420563978595271106701962634473840914, 97);
node_index
.insert(1098499015810170842401428216621470177488952811780672364884710297364076372943, 98);
node_index
.insert(2666902303639302012507119689908308317608522901613536135678723310999647515155, 99);
node_index
.insert(907997515879651052705985194221621380802961721264372722705825219340461809200, 100);
node_index
.insert(2124360554325144308113106422635485756539471211141315552843423768396084888273, 101);
node_index
.insert(3598736440043009208771817410113758019876931018927260161846683440123219507147, 102);
node_index
.insert(1237113034722832488580561245188430373504295256910735188987019984096012001931, 103);
node_index
.insert(884558344049768836371555446021588200903052780339208951904957 |
349404044037185, 104);
node_index
.insert(784280321344489256066716285882203121428790637989919760379274813665427427262, 105);
node_index
.insert(3472551952588748711709398308465335743810517871695257916614928877311914574241, 106);
node_index
.insert(1579363348100943961344032004617708767155021524242506190674861550786419896732, 107);
node_index
.insert(653576968777651719072715499492112313607520878545254037043893560183879857489, 108);
node_index
.insert(2633327961579170199842757290989312779085828750765842327985383652720803061926, 109);
node_index
.insert(3101204920253220343970782457572784926765600523633379722044614528209389590915, 110);
node_index
.insert(2537565394330405662800880050062241097694806466900452037378113841155978555645, 111);
node_index
.insert(306955559655552244989220345789093187601563118591829582730637833945761653350, 112);
node_index
.insert(1144065212212058748489308207801098564095305699242880891977316839573431241916, 113);
node_index
.insert(3478181491851418723342103101321490659650934149094649769124337426850038155270, 114);
node_index
.insert(3419621624676637660673415219086314486713019053519954317586073983685881930356, 115);
node_index
.insert(2426908011370291613447136873176769136554489197972200481728552402228021778402, 116);
node_index
.insert(1916122042123370178944690083048900704842269230325086549679099089416174875473, 117);
node_index
.insert(2057207652658215393591191155928140567561900227203223756539551876829334137660, 118);
node_index
.insert(2722034389703601317070746005702467061064354401688341549606678773616189196490, 119);
node_index
.insert(1171026027377763359814377926117880688616494219551682642535759838199732407496, 120);
node_index
.insert(3507234282031533800397666430789917374211847440333243952151005899337152633413, 121);
node_index
.insert(591003147462937848375161803108517142253138 |
969543815135207326321181858185919, 122);
node_index
.insert(182069734527202013451813026473135702900640769187641767871411473365447302169, 123);
node_index
.insert(1195243682249232878341146428166676460720423167409013083888435705219134747702, 124);
node_index
.insert(1793425644853312386902998134061844248823841892125424765064687913085130719534, 125);
node_index
.insert(1983622665815164792580256365519803214027269990384198703315493315153573288434, 126);
node_index
.insert(3615973154491344159350153395208055142342062736505558158666764642048838175685, 127);
node_index
.insert(2751715913626909804252433699602081411293721754810298670422380863932998088133, 128);
node_index
.insert(186918881712189523740089713555196200069231794627360499557319265374750577226, 129);
node_index
.insert(696585542544434929491503209053317581175146475161262066468664234437983008675, 130);
node_index
.insert(4359830495913805154545225899592517767672472055784183911796827820518038513, 131);
node_index
.insert(2954335207058000607751727656601539819316106074875304820535376873121805433820, 132);
node_index
.insert(2510390039949230255082316953804013731253145558531652907601250263563528226672, 133);
node_index
.insert(3226995230854300551967642178527450300960499043510855212238369890580256668532, 134);
node_index
.insert(1620924075233065517364532267959798304439946408626316544761884056227131075831, 135);
node_index
.insert(1610900122192929153657761847202689179268074338802437933866337242354758101660, 136);
node_index
.insert(2565949095169598991903537465065584077778440646580025930326495506484329892725, 137);
node_index
.insert(1012362975819634411571869839734809106575285344002573666983595104659295812607, 138);
node_index
.insert(242312010918799555845832460483650516749990744287009628468613253461264531026, 139);
node_index
.insert(1104776796569046483584574115 |
975216172161469015460244982207905888870418040487, 140);
node_index
.insert(3289555912992777681578950209252840071327866822704829766247386311885634446673, 141);
node_index
.insert(3133389957643610781371406448279843175887428913359743769920083259111437722268, 142);
node_index
.insert(1169918710119352022244140656086831769713178729571654411898266328562003734517, 143);
node_index
.insert(3592039235252149652556167686570045881877115549259769455422056097903987237819, 144);
node_index
.insert(2048175709145840597887667330964815895803568760936075562647625937161113445908, 145);
node_index
.insert(602222645962845554276438041138511866776339653340605661136009451417275008940, 146);
node_index
.insert(3318742320906017551291978242369663702298606650330380959683585594592748661010, 147);
node_index
.insert(564160996724923690963741657975239836484028160385417016805513722318839327322, 148);
node_index
.insert(656294390376267384135628810815504467149264887388377312825033341338166573620, 149);
node_index
.insert(1201592236750942207412694706123654466634588634474700675083122904145559965915, 150);
node_index
.insert(2141408926815137181004274624388915700231991905288681935478972043994347966006, 151);
node_index
.insert(1440847977042239464860406726605567303568767649154338464116083965986084755262, 152);
node_index
.insert(950585553138591375958592507876257987416844837045084288783892644487908218679, 153);
node_index
.insert(257643451533833048856069434258149588745628261389615631070776723485957908127, 154);
let atts = TreeEnsembleAttributes {
nodes_falsenodeids,
nodes_featureids,
nodes_missing_value_tracks_true,
nodes_modes,
nodes_nodeids,
nodes_treeids,
nodes_truenodeids,
nodes_values
};
let mut ensemble: TreeEnsemble<FP16x16> = TreeEnsemble {
atts, tree_ids, root_index, node_index
};
let mut |
classifier: TreeEnsembleClassifier<FP16x16> = TreeEnsembleClassifier {
ensemble,
class_ids,
class_nodeids,
class_treeids,
class_weights,
classlabels,
base_values,
post_transform
};
let mut X = TensorTrait::new(
array![1, 9].span(),
array![
FP16x16 { mag: 39321, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 52428, sign: false },
FP16x16 { mag: 16384, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 65536, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 16384, sign: false },
FP16x16 { mag: 0, sign: false },
]
.span()
);
(classifier, X)
} |
use orion::numbers::FP16x16;
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
use orion::operators::ml::tree_ensemble::core::{NODE_MODES, TreeEnsembleAttributes, TreeEnsemble};
use orion::operators::ml::tree_ensemble::tree_ensemble_regressor::{
TreeEnsembleRegressor, POST_TRANSFORM, TreeEnsembleRegressorTrait, AGGREGATE_FUNCTION
};
use orion::operators::matrix::{MutMatrix, MutMatrixImpl};
use orion::operators::tensor::implementations::tensor_fp16x16::relative_eq;
use core::debug::PrintTrait; |
fn test_tree_ensemble_regressor_SUM() {
let (mut regressor, X) = tree_ensemble_regressor_helper(AGGREGATE_FUNCTION::SUM);
let mut res = TreeEnsembleRegressorTrait::predict(regressor, X);
assert(
relative_eq(@res.get(0, 0).unwrap(), @FP16x16 { mag: 37809, sign: false }) == true,
'res[0, 0] = 37809'
);
assert(
relative_eq(@res.get(1, 0).unwrap(), @FP16x16 { mag: 37809, sign: false }) == true,
'res[1, 0] = 37809'
);
assert(
relative_eq(@res.get(2, 0).unwrap(), @FP16x16 { mag: 37809, sign: false }) == true,
'res[2, 0] = 37809'
);
} |
fn test_tree_ensemble_regressor_AVERAGE() {
let (mut regressor, X) = tree_ensemble_regressor_helper(AGGREGATE_FUNCTION::AVERAGE);
let mut res = TreeEnsembleRegressorTrait::predict(regressor, X);
assert(
relative_eq(@res.get(0, 0).unwrap(), @FP16x16 { mag: 18904, sign: false }) == true,
'res[0, 0] = 18904'
);
assert(
relative_eq(@res.get(1, 0).unwrap(), @FP16x16 { mag: 18904, sign: false }) == true,
'res[1, 0] = 18904'
);
assert(
relative_eq(@res.get(2, 0).unwrap(), @FP16x16 { mag: 18904, sign: false }) == true,
'res[2, 0] = 18904'
);
} |
fn test_tree_ensemble_regressor_MIN() {
let (mut regressor, X) = tree_ensemble_regressor_helper(AGGREGATE_FUNCTION::MIN);
let mut res = TreeEnsembleRegressorTrait::predict(regressor, X);
assert(
relative_eq(@res.get(0, 0).unwrap(), @FP16x16 { mag: 5041, sign: false }) == true,
'res[0, 0] = 5041'
);
assert(
relative_eq(@res.get(1, 0).unwrap(), @FP16x16 { mag: 5041, sign: false }) == true,
'res[1, 0] = 5041'
);
assert(
relative_eq(@res.get(2, 0).unwrap(), @FP16x16 { mag: 5041, sign: false }) == true,
'res[2, 0] = 5041'
);
} |
fn test_tree_ensemble_regressor_MAX() {
let (mut regressor, X) = tree_ensemble_regressor_helper(AGGREGATE_FUNCTION::MAX);
let mut res = TreeEnsembleRegressorTrait::predict(regressor, X);
assert(
relative_eq(@res.get(0, 0).unwrap(), @FP16x16 { mag: 32768, sign: false }) == true,
'res[0, 0] = 32768'
);
assert(
relative_eq(@res.get(1, 0).unwrap(), @FP16x16 { mag: 32768, sign: false }) == true,
'res[1, 0] = 32768'
);
assert(
relative_eq(@res.get(2, 0).unwrap(), @FP16x16 { mag: 32768, sign: false }) == true,
'res[2, 0] = 32768'
);
}
fn tree_ensemble_regressor_helper(
agg: AGGREGATE_FUNCTION
) -> (TreeEnsembleRegressor<FP16x16>, Tensor<FP16x16>) {
let n_targets: usize = 1;
let aggregate_function = agg;
let nodes_falsenodeids: Span<usize> = array![4, 3, 0, 0, 0, 2, 0, 4, 0, 0].span();
let nodes_featureids: Span<usize> = array![0, 2, 0, 0, 0, 0, 0, 2, 0, 0].span();
let nodes_missing_value_tracks_true: Span<usize> = array![0, 0, 0, 0, 0, 0, 0, 0, 0, 0].span();
let nodes_modes: Span<NODE_MODES> = array![
NODE_MODES::BRANCH_LEQ,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::BRANCH_LEQ,
NODE_MODES::LEAF,
NODE_MODES::LEAF
]
.span();
let nodes_nodeids: Span<usize> = array![0, 1, 2, 3, 4, 0, 1, 2, 3, 4].span();
let nodes_treeids: Span<usize> = array![0, 0, 0, 0, 0, 1, 1, 1, 1, 1].span();
let nodes_truenodeids: Span<usize> = array![1, 2, 0, 0, 0, 1, 0, 3, 0, 0].span();
let nodes_values: Span<FP16x16> = array![
FP16x16 { mag: 17462, sign: false },
FP16x16 { mag: 40726, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 47240, sign: true },
FP16x16 { mag: 0, sign: false },
FP16x16 { m |
ag: 36652, sign: true },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 0, sign: false }
]
.span();
let target_ids: Span<usize> = array![0, 0, 0, 0, 0, 0].span();
let target_nodeids: Span<usize> = array![2, 3, 4, 1, 3, 4].span();
let target_treeids: Span<usize> = array![0, 0, 0, 1, 1, 1].span();
let target_weights: Span<FP16x16> = array![
FP16x16 { mag: 5041, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 32768, sign: false },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 18724, sign: false },
FP16x16 { mag: 32768, sign: false }
]
.span();
let base_values: Option<Span<FP16x16>> = Option::None;
let post_transform = POST_TRANSFORM::NONE;
let tree_ids: Span<usize> = array![0, 1].span();
let mut root_index: Felt252Dict<usize> = Default::default();
root_index.insert(0, 0);
root_index.insert(1, 5);
let mut node_index: Felt252Dict<usize> = Default::default();
node_index
.insert(2089986280348253421170679821480865132823066470938446095505822317253594081284, 0);
node_index
.insert(2001140082530619239661729809084578298299223810202097622761632384561112390979, 1);
node_index
.insert(2592670241084192212354027440049085852792506518781954896144296316131790403900, 2);
node_index
.insert(2960591271376829378356567803618548672034867345123727178628869426548453833420, 3);
node_index
.insert(458933264452572171106695256465341160654132084710250671055261382009315664425, 4);
node_index
.insert(1089549915800264549621536909767699778745926517555586332772759280702396009108, 5);
node_index
.insert(1321142004022994845681377299801403567378503530250467610343381590909832171180, 6);
node_index
.insert(2592987851775965742543459319508348457290966253241455514226127639100457844774, 7);
node_index
.insert(2492755623019086109032247218615964389726368532160653497039005814484393419348, 8);
node |
_index
.insert(1323616023845704258113538348000047149470450086307731200728039607710316625916, 9);
let atts = TreeEnsembleAttributes {
nodes_falsenodeids,
nodes_featureids,
nodes_missing_value_tracks_true,
nodes_modes,
nodes_nodeids,
nodes_treeids,
nodes_truenodeids,
nodes_values
};
let mut ensemble: TreeEnsemble<FP16x16> = TreeEnsemble {
atts, tree_ids, root_index, node_index
};
let mut regressor: TreeEnsembleRegressor<FP16x16> = TreeEnsembleRegressor {
ensemble,
target_ids,
target_nodeids,
target_treeids,
target_weights,
base_values,
n_targets,
aggregate_function,
post_transform
};
let mut X: Tensor<FP16x16> = TensorTrait::new(
array![3, 3].span(),
array![
FP16x16 { mag: 32768, sign: true },
FP16x16 { mag: 26214, sign: true },
FP16x16 { mag: 19660, sign: true },
FP16x16 { mag: 13107, sign: true },
FP16x16 { mag: 6553, sign: true },
FP16x16 { mag: 0, sign: false },
FP16x16 { mag: 6553, sign: false },
FP16x16 { mag: 13107, sign: false },
FP16x16 { mag: 19660, sign: false },
]
.span()
);
(regressor, X)
} |
mod abs_fp16x16;
mod abs_fp8x23;
mod abs_i32;
mod abs_i8;
mod acos_fp16x16;
mod acos_fp8x23;
mod acosh_fp16x16;
mod acosh_fp8x23;
mod add_fp16x16;
mod add_fp16x16_broadcast;
mod add_fp8x23;
mod add_fp8x23_broadcast;
mod add_i32;
mod add_i32_broadcast;
mod add_i8;
mod add_i8_broadcast;
mod add_u32;
mod add_u32_broadcast;
mod argmin_fp16x16_1D_default;
mod argmin_fp16x16_1D_keepdims_false;
mod argmin_fp16x16_1D_last_index;
mod argmin_fp16x16_2D_default;
mod argmin_fp16x16_2D_keepdims_false;
mod argmin_fp16x16_2D_last_index;
mod argmin_fp16x16_3D_default;
mod argmin_fp16x16_3D_keepdims_false;
mod argmin_fp16x16_3D_last_index;
mod argmin_fp8x23_1D_default;
mod argmin_fp8x23_1D_keepdims_false;
mod argmin_fp8x23_1D_last_index;
mod argmin_fp8x23_2D_default;
mod argmin_fp8x23_2D_keepdims_false;
mod argmin_fp8x23_2D_last_index;
mod argmin_fp8x23_3D_default;
mod argmin_fp8x23_3D_keepdims_false;
mod argmin_fp8x23_3D_last_index;
mod argmin_i32_1D_default;
mod argmin_i32_1D_keepdims_false;
mod argmin_i32_1D_last_index;
mod argmin_i32_2D_default;
mod argmin_i32_2D_keepdims_false;
mod argmin_i32_2D_last_index;
mod argmin_i32_3D_default;
mod argmin_i32_3D_keepdims_false;
mod argmin_i32_3D_last_index;
mod argmin_i8_1D_default;
mod argmin_i8_1D_keepdims_false;
mod argmin_i8_1D_last_index;
mod argmin_i8_2D_default;
mod argmin_i8_2D_keepdims_false;
mod argmin_i8_2D_last_index;
mod argmin_i8_3D_default;
mod argmin_i8_3D_keepdims_false;
mod argmin_i8_3D_last_index;
mod argmin_u32_1D_default;
mod argmin_u32_1D_keepdims_false;
mod argmin_u32_1D_last_index;
mod argmin_u32_2D_default;
mod argmin_u32_2D_keepdims_false;
mod argmin_u32_2D_last_index;
mod argmin_u32_3D_default;
mod argmin_u32_3D_keepdims_false;
mod argmin_u32_3D_last_index;
mod asin_fp16x16;
mod asin_fp8x23;
mod asinh_fp16x16;
mod asinh_fp8x23;
mod atan_fp16x16;
mod atan_fp8x23;
mod ceil_fp16x16;
mod ceil_fp8x23;
mod concat_fp16x16_1d;
mod concat_fp16x16_2d;
mod concat_fp16x16_3d_default;
mod concat_fp16x16_3d_axis_1;
mod concat_fp16x16_3d_axis_2;
mod concat_fp16x16_3d_three_ |
tensors_axis_1;
mod concat_fp16x16_3d_three_tensors_axis_2;
mod concat_fp8x23_1d;
mod concat_fp8x23_2d;
mod concat_fp8x23_3d_default;
mod concat_fp8x23_3d_axis_1;
mod concat_fp8x23_3d_axis_2;
mod concat_fp8x23_3d_three_tensors_axis_1;
mod concat_fp8x23_3d_three_tensors_axis_2;
mod concat_i32_1d;
mod concat_i32_2d;
mod concat_i32_3d_default;
mod concat_i32_3d_axis_1;
mod concat_i32_3d_axis_2;
mod concat_i32_3d_three_tensors_axis_1;
mod concat_i32_3d_three_tensors_axis_2;
mod concat_i8_1d;
mod concat_i8_2d;
mod concat_i8_3d_default;
mod concat_i8_3d_axis_1;
mod concat_i8_3d_axis_2;
mod concat_i8_3d_three_tensors_axis_1;
mod concat_i8_3d_three_tensors_axis_2;
mod concat_u32_1d;
mod concat_u32_2d;
mod concat_u32_3d_default;
mod concat_u32_3d_axis_1;
mod concat_u32_3d_axis_2;
mod concat_u32_3d_three_tensors_axis_1;
mod concat_u32_3d_three_tensors_axis_2;
mod cos_fp16x16;
mod cos_fp8x23;
mod cosh_fp16x16;
mod cosh_fp8x23;
mod cumsum_fp16x16_1d_default;
mod cumsum_fp16x16_1d_exclusive;
mod cumsum_fp16x16_1d_reverse;
mod cumsum_fp16x16_1d_reverse_exclusive;
mod cumsum_fp16x16_2d_axis_0;
mod cumsum_fp16x16_2d_axis_1;
mod cumsum_fp8x23_1d_default;
mod cumsum_fp8x23_1d_exclusive;
mod cumsum_fp8x23_1d_reverse;
mod cumsum_fp8x23_1d_reverse_exclusive;
mod cumsum_fp8x23_2d_axis_0;
mod cumsum_fp8x23_2d_axis_1;
mod cumsum_i32_1d_default;
mod cumsum_i32_1d_exclusive;
mod cumsum_i32_1d_reverse;
mod cumsum_i32_1d_reverse_exclusive;
mod cumsum_i32_2d_axis_0;
mod cumsum_i32_2d_axis_1;
mod cumsum_i8_1d_default;
mod cumsum_i8_1d_exclusive;
mod cumsum_i8_1d_reverse;
mod cumsum_i8_1d_reverse_exclusive;
mod cumsum_i8_2d_axis_0;
mod cumsum_i8_2d_axis_1;
mod cumsum_u32_1d_default;
mod cumsum_u32_1d_exclusive;
mod cumsum_u32_1d_reverse;
mod cumsum_u32_1d_reverse_exclusive;
mod cumsum_u32_2d_axis_0;
mod cumsum_u32_2d_axis_1;
mod div_fp16x16;
mod div_fp16x16_broadcast;
mod div_fp8x23;
mod div_fp8x23_broadcast;
mod div_i32;
mod div_i32_broadcast;
mod div_i8;
mod div_i8_broadcast;
mod div_u32;
mod div_u32_broadcast;
mod equal_fp16x16;
mod equal_f |
p16x16_broadcast;
mod equal_fp8x23;
mod equal_fp8x23_broadcast;
mod equal_i32;
mod equal_i32_broadcast;
mod equal_i8;
mod equal_i8_broadcast;
mod equal_u32;
mod equal_u32_broadcast;
mod exp_fp16x16;
mod exp_fp8x23;
mod less_equal_fp16x16;
mod less_equal_fp16x16_broadcast;
mod less_equal_fp8x23;
mod less_equal_fp8x23_broadcast;
mod less_equal_i32;
mod less_equal_i32_broadcast;
mod less_equal_i8;
mod less_equal_i8_broadcast;
mod less_equal_u32;
mod less_equal_u32_broadcast;
mod greater_fp16x16;
mod greater_fp16x16_broadcast;
mod greater_fp8x23;
mod greater_fp8x23_broadcast;
mod greater_i32;
mod greater_i32_broadcast;
mod greater_i8;
mod greater_i8_broadcast;
mod greater_u32;
mod greater_u32_broadcast;
mod leaky_relu_fp16x16;
mod leaky_relu_fp8x23;
mod linear_fp16x16;
mod linear_fp8x23;
mod linear_i32;
mod linear_i8;
mod linear_u32;
mod log_fp16x16;
mod log_fp8x23;
mod logsoftmax_fp16x16_axis_0;
mod logsoftmax_fp16x16_axis_1;
mod logsoftmax_fp8x23_axis_0;
mod logsoftmax_fp8x23_axis_1;
mod matmul_fp16x16_1d;
mod matmul_fp16x16_2x2;
mod matmul_fp16x16_2x1;
mod matmul_fp16x16_1x2;
mod matmul_fp8x23_1d;
mod matmul_fp8x23_2x2;
mod matmul_fp8x23_2x1;
mod matmul_fp8x23_1x2;
mod matmul_i32_1d;
mod matmul_i32_2x2;
mod matmul_i32_2x1;
mod matmul_i32_1x2;
mod matmul_i8_1d;
mod matmul_i8_2x2;
mod matmul_i8_2x1;
mod matmul_i8_1x2;
mod matmul_u32_1d;
mod matmul_u32_2x2;
mod matmul_u32_2x1;
mod matmul_u32_1x2;
mod mul_fp16x16;
mod mul_fp16x16_broadcast;
mod mul_fp8x23;
mod mul_fp8x23_broadcast;
mod mul_i32;
mod mul_i32_broadcast;
mod mul_i8;
mod mul_i8_broadcast;
mod mul_u32;
mod mul_u32_broadcast;
mod or_fp16x16;
mod or_fp16x16_broadcast;
mod or_fp8x23;
mod or_fp8x23_broadcast;
mod or_i32;
mod or_i32_broadcast;
mod or_i8;
mod or_i8_broadcast;
mod or_u32;
mod or_u32_broadcast;
mod relu_fp16x16;
mod relu_fp8x23;
mod relu_i32;
mod relu_i8;
mod sigmoid_fp16x16;
mod sigmoid_fp8x23;
mod sin_fp16x16;
mod sin_fp8x23;
mod sinh_fp16x16;
mod sinh_fp8x23;
mod softplus_fp8x23;
mod softplus_fp16x16;
mod softsign_fp8x23;
mod softsign_fp16x16;
m |
od sqrt_fp16x16;
mod sqrt_fp8x23;
mod sub_fp16x16;
mod sub_fp16x16_broadcast;
mod sub_fp8x23;
mod sub_fp8x23_broadcast;
mod sub_i32;
mod sub_i32_broadcast;
mod sub_i8;
mod sub_i8_broadcast;
mod sub_u32;
mod sub_u32_broadcast;
mod tanh_fp16x16;
mod tanh_fp8x23;
mod transpose_fp16x16_2d;
mod transpose_fp16x16_3d;
mod transpose_fp8x23_2d;
mod transpose_fp8x23_3d;
mod transpose_i32_2d;
mod transpose_i32_3d;
mod transpose_i8_2d;
mod transpose_i8_3d;
mod transpose_u32_2d;
mod transpose_u32_3d;
mod xor_fp16x16;
mod xor_fp16x16_broadcast;
mod xor_fp8x23;
mod xor_fp8x23_broadcast;
mod xor_i32;
mod xor_i32_broadcast;
mod xor_i8;
mod xor_i8_broadcast;
mod xor_u32;
mod xor_u32_broadcast;
mod greater_equal_fp16x16;
mod greater_equal_fp16x16_broadcast;
mod greater_equal_fp8x23;
mod greater_equal_fp8x23_broadcast;
mod greater_equal_i32;
mod greater_equal_i32_broadcast;
mod greater_equal_i8;
mod greater_equal_i8_broadcast;
mod greater_equal_u32;
mod greater_equal_u32_broadcast;
mod slice_fp16x16_2d;
mod slice_fp16x16_3d;
mod slice_fp8x23_2d;
mod slice_fp8x23_3d;
mod slice_i32_2d;
mod slice_i32_3d;
mod slice_i8_2d;
mod slice_i8_3d;
mod slice_u32_2d;
mod slice_u32_3d;
mod nonzero_fp16x16_2d;
mod nonzero_fp16x16_3d;
mod nonzero_fp8x23_2d;
mod nonzero_fp8x23_3d;
mod nonzero_i32_2d;
mod nonzero_i32_3d;
mod nonzero_i8_2d;
mod nonzero_i8_3d;
mod nonzero_u32_2d;
mod nonzero_u32_3d;
mod squeeze_fP16x16;
mod squeeze_fP8x23;
mod squeeze_i32;
mod squeeze_i8;
mod squeeze_u32;
mod unsqueeze_fp16x16_2d;
mod unsqueeze_fp16x16_3d;
mod unsqueeze_fp8x23_2d;
mod unsqueeze_fp8x23_3d;
mod unsqueeze_i32_2d;
mod unsqueeze_i32_3d;
mod unsqueeze_i8_2d;
mod unsqueeze_i8_3d;
mod unsqueeze_u32_2d;
mod unsqueeze_u32_3d;
mod sign_fP16x16;
mod sign_fP8x23;
mod sign_fail;
mod sign_i32;
mod sign_i8;
mod clip_fp16x16_2d;
mod clip_fp16x16_3d;
mod clip_fp8x23_2d;
mod clip_fp8x23_3d;
mod clip_i32_2d;
mod clip_i32_3d;
mod clip_i8_2d;
mod clip_i8_3d;
mod clip_u32_2d;
mod clip_u32_3d;
mod identity_fP16x16;
mod identity_fP8x23;
mod identity_i32;
mod identity_i8;
mod ide |
ntity_u32;
mod thresholded_relu_fp16x16;
mod thresholded_relu_fp8x23;
mod hard_sigmoid_fp8x23;
mod hard_sigmoid_fp16x16;
mod neg_fp16x16;
mod neg_fp8x23;
mod neg_i32;
mod neg_i8;
mod gemm_all_attributes;
mod gemm_alpha;
mod gemm_beta;
mod gemm_default_matrix_bias;
mod gemm_default_vector_bias;
mod gemm_default_no_bias;
mod gemm_transposeA;
mod gemm_transposeB;
mod min_fp16x16_three_tensors;
mod min_fp16x16_broadcast_three_tensors;
mod min_fp16x16_two_tensors;
mod min_fp16x16_broadcast_two_tensors;
mod min_fp8x23_three_tensors;
mod min_fp8x23_broadcast_three_tensors;
mod min_fp8x23_two_tensors;
mod min_fp8x23_broadcast_two_tensors;
mod min_i32_three_tensors;
mod min_i32_broadcast_three_tensors;
mod min_i32_two_tensors;
mod min_i32_broadcast_two_tensors;
mod min_i8_three_tensors;
mod min_i8_broadcast_three_tensors;
mod min_i8_two_tensors;
mod min_i8_broadcast_two_tensors;
mod min_u32_three_tensors;
mod min_u32_broadcast_three_tensors;
mod min_u32_two_tensors;
mod min_u32_broadcast_two_tensors;
mod where_fp16x16;
mod where_fp16x16_broadcast;
mod where_fp8x23;
mod where_fp8x23_broadcast;
mod where_i32;
mod where_i32_broadcast;
mod where_i8;
mod where_i8_broadcast;
mod where_u32;
mod where_u32_broadcast;
mod not_bool;
mod round_fp16x16;
mod round_fp8x23;
mod max_fp16x16_three_tensors;
mod max_fp16x16_broadcast_three_tensors;
mod max_fp16x16_two_tensors;
mod max_fp16x16_broadcast_two_tensors;
mod max_fp8x23_three_tensors;
mod max_fp8x23_broadcast_three_tensors;
mod max_fp8x23_two_tensors;
mod max_fp8x23_broadcast_two_tensors;
mod max_i32_three_tensors;
mod max_i32_broadcast_three_tensors;
mod max_i32_two_tensors;
mod max_i32_broadcast_two_tensors;
mod max_i8_three_tensors;
mod max_i8_broadcast_three_tensors;
mod max_i8_two_tensors;
mod max_i8_broadcast_two_tensors;
mod max_u32_three_tensors;
mod max_u32_broadcast_three_tensors;
mod max_u32_two_tensors;
mod max_u32_broadcast_two_tensors;
mod scatter_fp16x16_3d_default;
mod scatter_fp16x16_3d_axis1;
mod scatter_fp16x16_3d_axis1_add;
mod scatter_fp8x23_default;
mod scatte |
r_fp8x23_axis1;
mod scatter_fp8x23_mul;
mod scatter_i8_default;
mod scatter_i8_axis1;
mod scatter_i8_axis1_max;
mod scatter_u32_default;
mod scatter_u32_axis1;
mod scatter_u32_add;
mod array_feature_extractor_1D_i32;
mod array_feature_extractor_1D_fp8x23;
mod array_feature_extractor_1D_fp16x16;
mod array_feature_extractor_2D_i32;
mod array_feature_extractor_2D_fp8x23;
mod array_feature_extractor_2D_fp16x16;
mod array_feature_extractor_3D_i32;
mod array_feature_extractor_3D_fp8x23;
mod array_feature_extractor_3D_fp16x16;
mod binarizer_fp16x16;
mod binarizer_fp8x23;
mod tril_fp16x16;
mod tril_fp16x16_neg;
mod tril_fp16x16_one_row;
mod tril_fp16x16_out_neg;
mod tril_fp16x16_out_pos;
mod tril_fp16x16_pos;
mod tril_fp16x16_square;
mod tril_fp16x16_square_neg;
mod tril_fp16x16_zero;
mod triu_fp16x16;
mod triu_fp16x16_neg;
mod triu_fp16x16_one_row;
mod triu_fp16x16_out_neg;
mod triu_fp16x16_out_pos;
mod triu_fp16x16_pos;
mod triu_fp16x16_square;
mod triu_fp16x16_square_neg;
mod triu_fp16x16_zero;
mod tril_fp8x23;
mod tril_fp8x23_neg;
mod tril_fp8x23_one_row;
mod tril_fp8x23_out_neg;
mod tril_fp8x23_out_pos;
mod tril_fp8x23_pos;
mod tril_fp8x23_square;
mod tril_fp8x23_square_neg;
mod tril_fp8x23_zero;
mod triu_fp8x23;
mod triu_fp8x23_neg;
mod triu_fp8x23_one_row;
mod triu_fp8x23_out_neg;
mod triu_fp8x23_out_pos;
mod triu_fp8x23_pos;
mod triu_fp8x23_square;
mod triu_fp8x23_square_neg;
mod triu_fp8x23_zero;
mod tril_i32;
mod tril_neg_i32;
mod tril_i32_one_row;
mod tril_i32_out_neg;
mod tril_i32_out_pos;
mod tril_i32_pos;
mod tril_i32_square;
mod tril_i32_square_neg;
mod tril_i32_zero;
mod triu_i32;
mod triu_i32_neg;
mod triu_i32_one_row;
mod triu_i32_out_neg;
mod triu_i32_out_pos;
mod triu_i32_pos;
mod triu_i32_square;
mod triu_i32_square_neg;
mod triu_i32_zero;
mod tril_i8;
mod tril_i8_neg;
mod tril_i8_one_row;
mod tril_i8_out_neg;
mod tril_i8_out_pos;
mod tril_i8_pos;
mod tril_i8_square;
mod tril_i8_square_neg;
mod tril_i8_zero;
mod triu_i8;
mod triu_i8_neg;
mod triu_i8_one_row;
mod triu_i8_out_neg;
mod triu_i8_out_pos; |
mod triu_i8_pos;
mod triu_i8_square;
mod triu_i8_square_neg;
mod triu_i8_zero;
mod tril_u32;
mod tril_u32_neg;
mod tril_u32_one_row;
mod tril_u32_out_neg;
mod tril_u32_out_pos;
mod tril_u32_pos;
mod tril_u32_square;
mod tril_u32_square_neg;
mod tril_u32_zero;
mod triu_u32;
mod triu_u32_neg;
mod triu_u32_one_row;
mod triu_u32_out_neg;
mod triu_u32_out_pos;
mod triu_u32_pos;
mod triu_u32_square;
mod triu_u32_square_neg;
mod triu_u32_zero;
mod reduce_sum_square_fp16x16_export_do_not_keepdims;
mod reduce_sum_square_fp16x16_export_keepdims;
mod reduce_sum_square_fp16x16_export_negative_axes_keepdims;
mod reduce_sum_square_fp8x23_export_do_not_keepdims;
mod reduce_sum_square_fp8x23_export_keepdims;
mod reduce_sum_square_fp8x23_export_negative_axes_keepdims;
mod reduce_sum_square_i32_export_do_not_keepdims;
mod reduce_sum_square_i32_export_keepdims;
mod reduce_sum_square_i32_export_negative_axes_keepdims;
mod reduce_sum_square_i8_export_do_not_keepdims;
mod reduce_sum_square_i8_export_keepdims;
mod reduce_sum_square_i8_export_negative_axes_keepdims;
mod reduce_sum_square_u32_export_do_not_keepdims;
mod reduce_sum_square_u32_export_keepdims;
mod reduce_sum_square_u32_export_negative_axes_keepdims;
mod reduce_l2_fp16x16_export_do_not_keepdims;
mod reduce_l2_fp16x16_export_keepdims;
mod reduce_l2_fp16x16_export_negative_axes_keepdims;
mod reduce_l2_fp8x23_export_do_not_keepdims;
mod reduce_l2_fp8x23_export_keepdims;
mod reduce_l2_fp8x23_export_negative_axes_keepdims;
mod reduce_l1_fp16x16_export_do_not_keepdims;
mod reduce_l1_fp16x16_export_keepdims;
mod reduce_l1_fp16x16_export_negative_axes_keepdims;
mod reduce_l1_fp8x23_export_do_not_keepdims;
mod reduce_l1_fp8x23_export_keepdims;
mod reduce_l1_fp8x23_export_negative_axes_keepdims;
mod reduce_l1_i32_export_do_not_keepdims;
mod reduce_l1_i32_export_keepdims;
mod reduce_l1_i32_export_negative_axes_keepdims;
mod reduce_l1_i8_export_do_not_keepdims;
mod reduce_l1_i8_export_keepdims;
mod reduce_l1_i8_export_negative_axes_keepdims;
mod reduce_l1_u32_export_do_not_keepdims;
mo |
d reduce_l1_u32_export_keepdims;
mod reduce_l1_u32_export_negative_axes_keepdims;
mod reduce_prod_fp16x16_1D;
mod reduce_prod_fp16x16_2D_default;
mod reduce_prod_fp16x16_2D_keepdims;
mod reduce_prod_fp16x16_2D_axis_1;
mod reduce_prod_fp8x23_1D;
mod reduce_prod_fp8x23_2D_default;
mod reduce_prod_fp8x23_2D_keepdims;
mod reduce_prod_fp8x23_2D_axis_1;
mod reduce_prod_i32_1D;
mod reduce_prod_i32_2D_default;
mod reduce_prod_i32_2D_keepdims;
mod reduce_prod_i32_2D_axis_1;
mod reduce_prod_i8_1D;
mod reduce_prod_i8_2D_default;
mod reduce_prod_i8_2D_keepdims;
mod reduce_prod_i8_2D_axis_1;
mod reduce_prod_u32_1D;
mod reduce_prod_u32_2D_default;
mod reduce_prod_u32_2D_keepdims;
mod reduce_prod_u32_2D_axis_1;
mod sequence_length_fp16x16;
mod sequence_length_fp16x16_broadcast;
mod sequence_length_fp8x23;
mod sequence_length_fp8x23_broadcast;
mod sequence_length_i32;
mod sequence_length_i32_broadcast;
mod sequence_length_i8;
mod sequence_length_i8_broadcast;
mod sequence_length_u32;
mod sequence_length_u32_broadcast;
mod sequence_at_u32_positive;
mod sequence_at_u32_negative;
mod sequence_at_fp16x16_positive;
mod sequence_at_fp16x16_negative;
mod sequence_at_fp8x23_positive;
mod sequence_at_fp8x23_negative;
mod sequence_at_i32_positive;
mod sequence_at_i32_negative;
mod sequence_at_i8_positive;
mod sequence_at_i8_negative;
mod reduce_min_fp16x16_1D;
mod reduce_min_fp16x16_2D_default;
mod reduce_min_fp16x16_2D_keepdims;
mod reduce_min_fp16x16_2D_axis_1;
mod reduce_min_fp8x23_1D;
mod reduce_min_fp8x23_2D_default;
mod reduce_min_fp8x23_2D_keepdims;
mod reduce_min_fp8x23_2D_axis_1;
mod reduce_min_i32_1D;
mod reduce_min_i32_2D_default;
mod reduce_min_i32_2D_keepdims;
mod reduce_min_i32_2D_axis_1;
mod reduce_min_i8_1D;
mod reduce_min_i8_2D_default;
mod reduce_min_i8_2D_keepdims;
mod reduce_min_i8_2D_axis_1;
mod reduce_min_u32_1D;
mod reduce_min_u32_2D_default;
mod reduce_min_u32_2D_keepdims;
mod reduce_min_u32_2D_axis_1;
mod sequence_construct_fp16x16;
mod sequence_construct_fp8x23;
mod sequence_construct_i32;
mod sequence_construct_ |
i8;
mod sequence_construct_u32;
mod shrink_hard_fp16x16;
mod shrink_soft_fp16x16;
mod shrink_hard_fp8x23;
mod shrink_soft_fp8x23;
mod sequence_empty_fp16x16;
mod sequence_empty_fp8x23;
mod sequence_empty_i32;
mod sequence_empty_i8;
mod sequence_empty_u32;
mod reduce_mean_fp16x16_1D;
mod reduce_mean_fp16x16_2D_default;
mod reduce_mean_fp16x16_2D_keepdims;
mod reduce_mean_fp16x16_2D_axis_1;
mod reduce_mean_fp8x23_1D;
mod reduce_mean_fp8x23_2D_default;
mod reduce_mean_fp8x23_2D_keepdims;
mod reduce_mean_fp8x23_2D_axis_1;
mod reduce_mean_i32_1D;
mod reduce_mean_i32_2D_default;
mod reduce_mean_i32_2D_keepdims;
mod reduce_mean_i32_2D_axis_1;
mod reduce_mean_i8_1D;
mod reduce_mean_i8_2D_default;
mod reduce_mean_i8_2D_keepdims;
mod reduce_mean_i8_2D_axis_1;
mod reduce_mean_u32_1D;
mod reduce_mean_u32_2D_default;
mod reduce_mean_u32_2D_keepdims;
mod reduce_mean_u32_2D_axis_1;
mod pow_fp16x16;
mod pow_fp16x16_broadcast;
mod pow_fp8x23;
mod pow_fp8x23_broadcast;
mod sequence_erase_u32_positive;
mod sequence_erase_u32_negative;
mod sequence_erase_u32_empty;
mod sequence_erase_fp16x16_positive;
mod sequence_erase_fp16x16_negative;
mod sequence_erase_fp16x16_empty;
mod sequence_erase_fp8x23_positive;
mod sequence_erase_fp8x23_negative;
mod sequence_erase_fp8x23_empty;
mod sequence_erase_i32_positive;
mod sequence_erase_i32_negative;
mod sequence_erase_i32_empty;
mod sequence_erase_i8_positive;
mod sequence_erase_i8_negative;
mod sequence_erase_i8_empty;
mod sequence_insert_fp16x16;
mod sequence_insert_fp8x23;
mod sequence_insert_i32;
mod sequence_insert_i8;
mod sequence_insert_u32;
mod concat_from_sequence_fp8x23_new_axis_zero;
mod concat_from_sequence_fp8x23_new_axis_one;
mod concat_from_sequence_fp8x23_new_axis_default;
mod concat_from_sequence_fp16x16_new_axis_zero;
mod concat_from_sequence_fp16x16_new_axis_one;
mod concat_from_sequence_fp16x16_new_axis_default;
mod concat_from_sequence_i32_new_axis_zero;
mod concat_from_sequence_i32_new_axis_one;
mod concat_from_sequence_i32_new_axis_default;
mod concat_from_sequence_i8_ne |
w_axis_zero;
mod concat_from_sequence_i8_new_axis_one;
mod concat_from_sequence_i8_new_axis_default;
mod concat_from_sequence_u32_new_axis_zero;
mod concat_from_sequence_u32_new_axis_one;
mod concat_from_sequence_u32_new_axis_default;
mod is_nan_fp16x16;
mod is_nan_fp8x23;
mod is_inf_fp16x16;
mod is_inf_fp8x23;
mod is_inf_i32;
mod is_inf_i8;
mod is_inf_u32;
mod is_pos_inf_fp16x16;
mod is_neg_inf_fp16x16;
mod is_pos_inf_fp8x23;
mod is_neg_inf_fp8x23;
mod is_pos_inf_i32;
mod is_neg_inf_i32;
mod is_pos_inf_i8;
mod is_neg_inf_i8;
mod reduce_log_sum_fp8x23_export_do_not_keepdims;
mod reduce_log_sum_fp8x23_export_keepdims;
mod reduce_log_sum_fp8x23_export_negative_axes_keepdims;
mod reduce_log_sum_fp16x16_export_do_not_keepdims;
mod reduce_log_sum_fp16x16_export_keepdims;
mod reduce_log_sum_fp16x16_export_negative_axes_keepdims;
mod and_bool;
mod erf_fp16x16;
mod erf_fp8x23;
mod unique_fp16x16_without_axis_sorted;
mod unique_fp16x16_with_axis_zero_sorted;
mod unique_u32_without_axis_sorted;
mod unique_u32_without_axis_not_sorted;
mod unique_u32_with_axis_zero_sorted;
mod unique_u32_with_axis_zero_not_sorted;
mod unique_u32_with_axis_one_sorted;
mod unique_u32_with_axis_one_not_sorted;
mod gather_nd_fp16x16_3d_default;
mod gather_nd_fp16x16_3d_batch_dims1;
mod gather_nd_fp16x16_3d_batch_dims2;
mod gather_nd_fp8x23_3d_default;
mod gather_nd_fp8x23_3d_batch_dims1;
mod gather_nd_fp8x23_3d_batch_dims2;
mod gather_nd_i32_3d_default;
mod gather_nd_i32_3d_batch_dims1;
mod gather_nd_i32_3d_batch_dims2;
mod gather_nd_i8_3d_default;
mod gather_nd_i8_3d_batch_dims1;
mod gather_nd_u32_default;
mod gather_nd_u32_batch_dims1;
mod gather_nd_u32_batch_dims2;
mod resize_upsample_scales_nearest;
mod resize_downsample_scales_cubic;
mod resize_downsample_scales_cubic_A_n0p5_exclude_outside;
mod resize_downsample_scales_cubic_align_corners;
mod resize_upsample_scales_linear;
mod resize_downsample_scales_linear_align_corners;
mod resize_downsample_scales_nearest;
mod resize_upsample_scales_cubic;
mod resize_upsample_scales_cubic_A_n0p5_exclu |
de_outside;
mod resize_upsample_scales_cubic_align_corners;
mod resize_upsample_scales_cubic_asymmetric;
mod resize_upsample_scales_linear_align_corners;
mod resize_upsample_sizes_nearest;
mod resize_upsample_sizes_cubic;
mod resize_downsample_sizes_cubic;
mod resize_downsample_sizes_nearest;
mod resize_upsample_scales_linear_half_pixel_symmetric;
mod resize_downsample_scales_cubic_antialias;
mod resize_downsample_scales_linear_antialias;
mod resize_downsample_sizes_cubic_antialias;
mod resize_downsample_sizes_linear_pytorch_half_pixel;
mod resize_tf_crop_and_resize;
mod resize_tf_crop_and_resize_extrapolation_value;
mod resize_upsample_scales_nearest_axes_2_3;
mod resize_upsample_scales_nearest_axes_3_2;
mod resize_upsample_sizes_nearest_axes_2_3;
mod resize_upsample_sizes_nearest_ceil_half_pixel;
mod resize_upsample_sizes_nearest_floor_align_corners;
mod resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric;
mod resize_downsample_scales_linear_half_pixel_symmetric;
mod resize_downsample_sizes_nearest_not_larger;
mod resize_downsample_sizes_nearest_not_smaller;
mod resize_tf_crop_and_resize_axes_2_3;
mod resize_tf_crop_and_resize_axes_3_2;
mod resize_upsample_sizes_nearest_axes_3_2;
mod resize_upsample_sizes_nearest_not_larger;
mod resize_upsample_sizes_nearest_not_smaller;
mod compress_fp16x16_3d_default;
mod compress_fp16x16_3d_axis1;
mod compress_fp16x16_3d_axis2;
mod compress_fp16x16_3d_axis3;
mod compress_fp16x16_3d_noaxis;
mod compress_fp8x23_3d_default;
mod compress_fp8x23_3d_axis1;
mod compress_fp8x23_3d_axis2;
mod compress_i32_3d_default;
mod compress_i32_3d_axis1;
mod compress_i32_3d_axis2;
mod compress_i8_3d_default;
mod compress_i8_3d_axis1;
mod compress_i8_3d_axis2;
mod compress_u32_3d_default;
mod compress_u32_3d_axis1;
mod compress_u32_3d_axis2;
mod compress_u32_3d_axis2_2;
mod compress_u32_3d_axis3;
mod reduce_log_sum_exp_fp32x32_export_do_not_keepdims;
mod reduce_log_sum_exp_fp32x32_export_keepdims;
mod reduce_log_sum_exp_fp32x32_export_negative_axes_keepdims;
mod layer_normalization_defaul |
t_axis;
mod layer_normalization_4d_axis0;
mod layer_normalization_4d_axis1;
mod layer_normalization_4d_axis2;
mod layer_normalization_4d_axis3;
mod layer_normalization_3d_axis0_epsilon;
mod layer_normalization_3d_axis_negative_3_epsilon;
mod layer_normalization_3d_axis1_epsilon;
mod layer_normalization_3d_axis2_epsilon;
mod layer_normalization_4d_axis_negative_4;
mod layer_normalization_4d_axis_negative_3;
mod layer_normalization_4d_axis_negative_2;
mod layer_normalization_4d_axis_negative_1;
mod layer_normalization_3d_axis_negative_2_epsilon;
mod layer_normalization_3d_axis_negative_1_epsilon;
mod layer_normalization_test;
mod split_u32_1d_equal_parts;
mod split_u32_2d_equal_parts;
mod split_u32_zero_size;
mod split_u32_1d_variable_parts;
mod split_u32_2d_variable_parts;
mod split_u32_1d_uneven;
mod split_u32_2d_uneven;
mod split_fp16x16_1d_equal_parts;
mod split_fp16x16_1d_variable_parts;
mod split_fp16x16_2d_equal_parts;
mod split_fp16x16_2d_variable_parts;
mod split_fp16x16_zero_size;
mod split_fp16x16_1d_uneven;
mod split_fp16x16_2d_uneven;
mod grid_sample;
mod grid_sample_cubic;
mod grid_sample_aligncorners;
mod grid_sample_nearest;
mod grid_sample_nearest_aligncorner;
mod grid_sample_padding_border;
mod grid_sample_padding_reflection;
mod grid_sample_padding_zeros;
mod col2im;
mod col2im_5D;
mod col2im_dilations;
mod col2im_pads;
mod col2im_strides;
mod random_uniform_like_fp16x16;
mod random_uniform_like_fp8x23;
mod range_fp8x23;
mod range_fp16x16;
mod range_i32;
mod range_i8;
mod range_u32;
mod hann_window_fp8x23;
mod hann_window_fp16x16;
mod hamming_window_fp16x16;
mod hamming_window_fp8x23;
mod blackman_window_fp16x16;
mod blackman_window_fp8x23;
mod split_to_sequence_fp16x16_1d_equal_parts;
mod split_to_sequence_fp16x16_1d_variable_parts;
mod split_to_sequence_fp16x16_2d_equal_parts;
mod split_to_sequence_fp16x16_2d_variable_parts;
mod split_to_sequence_fp16x16_zero_size;
mod split_to_sequence_fp16x16_1d_uneven;
mod split_to_sequence_fp16x16_2d_uneven;
mod split_to_sequence_u32_1d_equal_parts;
mod spl |
it_to_sequence_u32_1d_variable_parts;
mod split_to_sequence_u32_2d_equal_parts;
mod split_to_sequence_u32_2d_variable_parts;
mod split_to_sequence_u32_zero_size;
mod split_to_sequence_u32_1d_uneven;
mod split_to_sequence_u32_2d_uneven;
mod split_to_sequence_2d_scalar;
mod split_to_sequence_2d_nokeepdims;
mod split_to_sequence_1d_nokeepdims;
mod reverse_sequence_fp16x16_batch_equal_parts;
mod reverse_sequence_fp16x16_time_equal_parts;
mod reverse_sequence_i32_batch_equal_parts;
mod reverse_sequence_i32_time_equal_parts;
mod reverse_sequence_i8_batch_equal_parts;
mod reverse_sequence_i8_time_equal_parts;
mod reverse_sequence_u32_4x4_batch;
mod reverse_sequence_u32_4x4_time;
mod reverse_sequence_u32_3x3_batch;
mod reverse_sequence_u32_3x3_time;
mod reverse_sequence_different_dimensions_4_5;
mod reverse_sequence_different_dimensions_2_4;
mod reverse_sequence_different_dimensions_1_6;
mod reverse_sequence_different_dimensions_3x9_batch;
mod reverse_sequence_different_dimensions_3x9_time;
mod conv_transpose;
mod conv_transpose_1d;
mod conv_transpose_3d;
mod conv_transpose_attributes;
mod conv_transpose_autopad_same;
mod conv_transpose_dilations;
mod conv_transpose_pads;
mod conv_transpose_group_2;
mod conv_transpose_group_2_image_3;
mod depth_to_space_fp16x16;
mod depth_to_space_fp8x23;
mod depth_to_space_i32;
mod depth_to_space_i8;
mod depth_to_space_u32;
mod space_to_depth_fp16x16;
mod space_to_depth_fp8x23;
mod space_to_depth_i32;
mod space_to_depth_i8;
mod space_to_depth_u32;
mod scatter_nd_fp16x16_3d_default;
mod scatter_nd_fp16x16_3d_add;
mod scatter_nd_fp16x16_3d_mul;
mod scatter_nd_fp16x16_3d_max;
mod scatter_nd_fp16x16_3d_min;
mod scatter_nd_fp8x23_3d_default;
mod scatter_nd_fp8x23_3d_add;
mod scatter_nd_fp8x23_3d_mul;
mod scatter_nd_fp8x23_3d_max;
mod scatter_nd_fp8x23_3d_min;
mod scatter_nd_u32_default;
mod scatter_nd_u32_add;
mod scatter_nd_u32_mul;
mod scatter_nd_u32_max;
mod scatter_nd_u32_min;
mod conv_2D_with_padding;
mod conv_1D_no_padding;
mod conv_1D_with_padding;
mod conv_3D_no_padding;
mod conv_3D_ |
with_padding;
mod conv_4D_no_padding;
mod conv_2D_with_2_groups;
mod conv_2D_with_autopad_same;
mod conv_2D_with_strides_asymmetric_padding;
mod conv_2D_with_strides_with_padding;
mod conv_4D_with_padding;
mod label_encoder_fp16x16_3d_default;
mod label_encoder_fp8x23_default;
mod label_encoder_i8_default;
mod label_encoder_i32_default;
mod label_encoder_u32_default;
mod gather_fp16x16_3d_default;
mod gather_fp16x16_3d_axis1;
mod gather_fp16x16_3d_axis2;
mod gather_negative_indices;
mod gather_negative_axis;
mod less_fp16x16;
mod less_fp16x16_broadcast;
mod less_fp8x23;
mod less_fp8x23_broadcast;
mod less_i32;
mod less_i32_broadcast;
mod less_i8;
mod less_i8_broadcast;
mod less_u32;
mod less_u32_broadcast;
mod reshape_extended_dims;
mod reshape_negative_dim;
mod reshape_negative_extended_dims;
mod reshape_one_dim;
mod reshape_reduced_dims;
mod reshape_reordered_all_dims;
mod reshape_reordered_last_dims;
mod reshape_zero_and_negative_dim;
mod reshape_zero_dim;
mod reduce_sum_default_axes_keepdims;
mod reduce_sum_empty_axes_input_noop;
mod reduce_sum_keep_dims;
mod reduce_sum_negative_axes_keepdims;
mod reduce_sum_no_keep_dims;
mod gather_elements_default;
mod gather_elements_axis1;
mod gather_elements_axis2;
mod gather_elements_negative_indices;
mod softmax_axis_0;
mod softmax_axis_1;
mod softmax_axis_2;
mod softmax_axis_minus_1;
mod argmax_default_axes_keepdims;
mod argmax_default_axes_keepdims_select_last_index;
mod argmax_keepdims;
mod argmax_keepdims_select_last_index;
mod argmax_negative_axis_keepdims;
mod argmax_negative_axis_keepdims_select_last_index;
mod argmax_no_keepdims;
mod argmax_no_keepdims_select_last_index; |
mod input_0;
mod output_0;
use orion::operators::tensor::FP16x16Tensor;
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::FP16x16TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
use core::array::{ArrayTrait, SpanTrait};
#[test]
#[available_gas(2000000000)]
fn test_abs_fp16x16() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.abs();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn input_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 3670016, sign: false });
data.append(FP16x16 { mag: 7208960, sign: true });
data.append(FP16x16 { mag: 3014656, sign: true });
data.append(FP16x16 { mag: 3932160, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn output_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 3670016, sign: false });
data.append(FP16x16 { mag: 7208960, sign: false });
data.append(FP16x16 { mag: 3014656, sign: false });
data.append(FP16x16 { mag: 3932160, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod output_0;
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::FP8x23Tensor;
use orion::operators::tensor::FP8x23TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
use core::array::{ArrayTrait, SpanTrait};
#[test]
#[available_gas(2000000000)]
fn test_abs_fp8x23() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.abs();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP8x23Tensor;
use orion::numbers::{FixedTrait, FP8x23};
fn input_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 738197504, sign: false });
data.append(FP8x23 { mag: 58720256, sign: false });
data.append(FP8x23 { mag: 285212672, sign: true });
data.append(FP8x23 { mag: 226492416, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP8x23Tensor;
use orion::numbers::{FixedTrait, FP8x23};
fn output_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 738197504, sign: false });
data.append(FP8x23 { mag: 58720256, sign: false });
data.append(FP8x23 { mag: 285212672, sign: false });
data.append(FP8x23 { mag: 226492416, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod output_0;
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::I32Tensor;
use orion::operators::tensor::I32TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
use core::array::{ArrayTrait, SpanTrait};
#[test]
#[available_gas(2000000000)]
fn test_abs_i32() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.abs();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::I32Tensor;
fn input_0() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(-35);
data.append(106);
data.append(91);
data.append(-12);
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::I32Tensor;
fn output_0() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(35);
data.append(106);
data.append(91);
data.append(12);
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod output_0;
use orion::operators::tensor::I8TensorPartialEq;
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::I8Tensor;
use orion::operators::tensor::{TensorTrait, Tensor};
use core::array::{ArrayTrait, SpanTrait};
#[test]
#[available_gas(2000000000)]
fn test_abs_i8() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.abs();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::I8Tensor;
fn input_0() -> Tensor<i8> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(-85);
data.append(100);
data.append(-90);
data.append(-40);
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::I8Tensor;
fn output_0() -> Tensor<i8> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(85);
data.append(100);
data.append(90);
data.append(40);
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod output_0;
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorSub};
use orion::operators::tensor::FP16x16TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
#[test]
#[available_gas(2000000000)]
fn test_acos_fp16x16() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.acos();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorSub};
use orion::numbers::{FixedTrait, FP16x16};
fn input_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 51202, sign: true });
data.append(FP16x16 { mag: 49944, sign: false });
data.append(FP16x16 { mag: 18761, sign: false });
data.append(FP16x16 { mag: 64655, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorSub};
use orion::numbers::{FixedTrait, FP16x16};
fn output_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 161711, sign: false });
data.append(FP16x16 { mag: 46154, sign: false });
data.append(FP16x16 { mag: 83915, sign: false });
data.append(FP16x16 { mag: 195133, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod output_0;
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorSub};
use orion::operators::tensor::FP8x23TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
#[test]
#[available_gas(2000000000)]
fn test_acos_fp8x23() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.acos();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorSub};
use orion::numbers::{FixedTrait, FP8x23};
fn input_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 3764690, sign: false });
data.append(FP8x23 { mag: 556457, sign: true });
data.append(FP8x23 { mag: 529360, sign: false });
data.append(FP8x23 { mag: 2252561, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorSub};
use orion::numbers::{FixedTrait, FP8x23};
fn output_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 9272682, sign: false });
data.append(FP8x23 { mag: 13733660, sign: false });
data.append(FP8x23 { mag: 12647081, sign: false });
data.append(FP8x23 { mag: 15457344, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod output_0;
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16TensorPartialEq;
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorSub};
use core::array::{ArrayTrait, SpanTrait};
#[test]
#[available_gas(2000000000)]
fn test_acosh_fp16x16() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.acosh();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorSub};
use orion::numbers::{FixedTrait, FP16x16};
fn input_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 278211, sign: false });
data.append(FP16x16 { mag: 184787, sign: false });
data.append(FP16x16 { mag: 83173, sign: false });
data.append(FP16x16 { mag: 258400, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorSub};
use orion::numbers::{FixedTrait, FP16x16};
fn output_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 139248, sign: false });
data.append(FP16x16 { mag: 111195, sign: false });
data.append(FP16x16 { mag: 47062, sign: false });
data.append(FP16x16 { mag: 134255, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod output_0;
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorSub};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP8x23TensorPartialEq;
use core::array::{ArrayTrait, SpanTrait};
#[test]
#[available_gas(2000000000)]
fn test_acosh_fp8x23() {
let input_0 = input_0::input_0();
let z = output_0::output_0();
let y = input_0.acosh();
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorSub};
use orion::numbers::{FixedTrait, FP8x23};
fn input_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 11868883, sign: false });
data.append(FP8x23 { mag: 28161016, sign: false });
data.append(FP8x23 { mag: 27794185, sign: false });
data.append(FP8x23 { mag: 28651727, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorSub};
use orion::numbers::{FixedTrait, FP8x23};
fn output_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(2);
shape.append(2);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 7399094, sign: false });
data.append(FP8x23 { mag: 15781079, sign: false });
data.append(FP8x23 { mag: 15665784, sign: false });
data.append(FP8x23 { mag: 15932759, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod input_1;
mod output_0;
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::FP16x16TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
#[test]
#[available_gas(2000000000)]
fn test_add_fp16x16() {
let input_0 = input_0::input_0();
let input_1 = input_1::input_1();
let z = output_0::output_0();
let y = input_0 + input_1;
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use orion::numbers::{FixedTrait, FP16x16};
fn input_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 131072, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use orion::numbers::{FixedTrait, FP16x16};
fn input_1() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use orion::numbers::{FixedTrait, FP16x16};
fn output_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 262144, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 196608, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 393216, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 262144, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod input_1;
mod output_0;
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::FP16x16TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
#[test]
#[available_gas(2000000000)]
fn test_add_fp16x16_broadcast() {
let input_0 = input_0::input_0();
let input_1 = input_1::input_1();
let z = output_0::output_0();
let y = input_0 + input_1;
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use orion::numbers::{FixedTrait, FP16x16};
fn input_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use orion::numbers::{FixedTrait, FP16x16};
fn input_1() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(1);
shape.append(3);
shape.append(1);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 65536, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd};
use orion::numbers::{FixedTrait, FP16x16};
fn output_0() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 196608, sign: true });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 196608, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 262144, sign: false });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 131072, sign: true });
data.append(FP16x16 { mag: 262144, sign: true });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 196608, sign: false });
data.append(FP16x16 { mag: 262144, sign: false });
data.append(FP16x16 { mag: 262144, sign: false });
data.append(FP16x16 { mag: 131072, sign: false });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 262144, sign: true });
data.append(FP16x16 { mag: 262144, sign: true });
data.append(FP16x16 { mag: 0, sign: false });
data.append(FP16x16 { mag: 196608, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 65536, sign: false });
data.append(FP16x16 { mag: 196608, sign: false });
data.append(FP16x16 { mag: 196608, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod input_1;
mod output_0;
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::operators::tensor::FP8x23TensorPartialEq;
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::{TensorTrait, Tensor};
#[test]
#[available_gas(2000000000)]
fn test_add_fp8x23() {
let input_0 = input_0::input_0();
let input_1 = input_1::input_1();
let z = output_0::output_0();
let y = input_0 + input_1;
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::numbers::{FixedTrait, FP8x23};
fn input_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::numbers::{FixedTrait, FP8x23};
fn input_1() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::numbers::{FixedTrait, FP8x23};
fn output_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 25165824, sign: false });
data.append(FP8x23 { mag: 33554432, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 25165824, sign: false });
data.append(FP8x23 { mag: 41943040, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 33554432, sign: false });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod input_1;
mod output_0;
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::operators::tensor::FP8x23TensorPartialEq;
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::{TensorTrait, Tensor};
#[test]
#[available_gas(2000000000)]
fn test_add_fp8x23_broadcast() {
let input_0 = input_0::input_0();
let input_1 = input_1::input_1();
let z = output_0::output_0();
let y = input_0 + input_1;
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::numbers::{FixedTrait, FP8x23};
fn input_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::numbers::{FixedTrait, FP8x23};
fn input_1() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(1);
shape.append(3);
shape.append(1);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
use orion::numbers::{FixedTrait, FP8x23};
fn output_0() -> Tensor<FP8x23> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 33554432, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 33554432, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 25165824, sign: false });
data.append(FP8x23 { mag: 33554432, sign: true });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 33554432, sign: true });
data.append(FP8x23 { mag: 33554432, sign: true });
data.append(FP8x23 { mag: 25165824, sign: true });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 16777216, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 8388608, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 16777216, sign: true });
data.append(FP8x23 { mag: 8388608, sign: true });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 0, sign: false });
data.append(FP8x23 { mag: 8388608, sign: true });
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod input_1;
mod output_0;
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::I32TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
#[test]
#[available_gas(2000000000)]
fn test_add_i32() {
let input_0 = input_0::input_0();
let input_1 = input_1::input_1();
let z = output_0::output_0();
let y = input_0 + input_1;
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
fn input_0() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(-1);
data.append(-1);
data.append(-2);
data.append(-2);
data.append(-1);
data.append(-3);
data.append(1);
data.append(-1);
data.append(0);
data.append(1);
data.append(2);
data.append(1);
data.append(2);
data.append(2);
data.append(0);
data.append(-2);
data.append(2);
data.append(2);
data.append(1);
data.append(-3);
data.append(2);
data.append(-2);
data.append(0);
data.append(-2);
data.append(2);
data.append(-2);
data.append(-1);
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
fn input_1() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(0);
data.append(1);
data.append(-2);
data.append(2);
data.append(-2);
data.append(-3);
data.append(-2);
data.append(1);
data.append(-3);
data.append(2);
data.append(0);
data.append(-3);
data.append(2);
data.append(1);
data.append(2);
data.append(0);
data.append(1);
data.append(-3);
data.append(-3);
data.append(0);
data.append(-1);
data.append(-2);
data.append(2);
data.append(-1);
data.append(2);
data.append(1);
data.append(2);
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
fn output_0() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(-1);
data.append(0);
data.append(-4);
data.append(0);
data.append(-3);
data.append(-6);
data.append(-1);
data.append(0);
data.append(-3);
data.append(3);
data.append(2);
data.append(-2);
data.append(4);
data.append(3);
data.append(2);
data.append(-2);
data.append(3);
data.append(-1);
data.append(-2);
data.append(-3);
data.append(1);
data.append(-4);
data.append(2);
data.append(-3);
data.append(4);
data.append(-1);
data.append(1);
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod input_1;
mod output_0;
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
use core::array::{ArrayTrait, SpanTrait};
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::I32TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
#[test]
#[available_gas(2000000000)]
fn test_add_i32_broadcast() {
let input_0 = input_0::input_0();
let input_1 = input_1::input_1();
let z = output_0::output_0();
let y = input_0 + input_1;
assert_eq(y, z);
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
fn input_0() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(-2);
data.append(-2);
data.append(0);
data.append(-1);
data.append(-3);
data.append(-3);
data.append(-3);
data.append(0);
data.append(1);
data.append(0);
data.append(-2);
data.append(-2);
data.append(1);
data.append(-2);
data.append(1);
data.append(2);
data.append(-3);
data.append(-1);
data.append(-2);
data.append(-3);
data.append(0);
data.append(0);
data.append(0);
data.append(-3);
data.append(1);
data.append(-2);
data.append(-2);
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
fn input_1() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(1);
shape.append(3);
shape.append(1);
let mut data = ArrayTrait::new();
data.append(-1);
data.append(0);
data.append(1);
TensorTrait::new(shape.span(), data.span())
}
|
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{I32Tensor, I32TensorAdd};
fn output_0() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(3);
shape.append(3);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(-3);
data.append(-3);
data.append(-1);
data.append(-1);
data.append(-3);
data.append(-3);
data.append(-2);
data.append(1);
data.append(2);
data.append(-1);
data.append(-3);
data.append(-3);
data.append(1);
data.append(-2);
data.append(1);
data.append(3);
data.append(-2);
data.append(0);
data.append(-3);
data.append(-4);
data.append(-1);
data.append(0);
data.append(0);
data.append(-3);
data.append(2);
data.append(-1);
data.append(-1);
TensorTrait::new(shape.span(), data.span())
}
|
mod input_0;
mod input_1;
mod output_0;
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{I8Tensor, I8TensorAdd};
use orion::utils::{assert_eq, assert_seq_eq};
use orion::operators::tensor::I8TensorPartialEq;
use orion::operators::tensor::{TensorTrait, Tensor};
#[test]
#[available_gas(2000000000)]
fn test_add_i8() {
let input_0 = input_0::input_0();
let input_1 = input_1::input_1();
let z = output_0::output_0();
let y = input_0 + input_1;
assert_eq(y, z);
}
|
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