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mcDandy-more_math/tests/test_unified_math.py
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import sys
import os
import torch
import math
# Ensure we can import the module
_here = os.path.abspath(os.path.dirname(__file__))
_project_root = os.path.abspath(os.path.join(_here, os.pardir))
if _project_root not in sys.path:
sys.path.insert(0, _project_root)
# Import visitor
from more_math.Parser.UnifiedMathVisitor import UnifiedMathVisitor
from more_math.Parser.MathExprLexer import MathExprLexer
from more_math.Parser.MathExprParser import MathExprParser
from antlr4 import InputStream, CommonTokenStream
def parse_and_visit(expr_str, variables):
lexer = MathExprLexer(InputStream(expr_str))
stream = CommonTokenStream(lexer)
parser = MathExprParser(stream)
tree = parser.start()
# We might need to pass shape/device if UnifiedMathVisitor requires it for tensor creation
# For now assuming it can infer or defaults.
# The original TensorEvalVisitor required shape. Unified might need it for "1.0" -> Tensor promotion cases?
# Or maybe "1.0" stays scalar till needed?
# Let's assume we pass a default shape/device if needed, but for scalar tests we might not need it.
shape = (1, 1, 1, 1) # Dummy shape
visitor = UnifiedMathVisitor(variables, shape)
return visitor.visit(tree)
def test_scalar_ops():
vars = {"a": 2.0, "b": 3.0}
assert parse_and_visit("a + b", vars) == 5.0
assert parse_and_visit("a * b", vars) == 6.0
assert parse_and_visit("sin(0)", vars) == 0.0
assert parse_and_visit("smax(a, b)", vars) == 3.0
assert parse_and_visit("step(2, 1)", vars) == 1.0
assert parse_and_visit("step(0, 1)", vars) == 0.0
assert parse_and_visit("clamp(5, 0, 10)", vars) == 5.0
assert parse_and_visit("clamp(-5, 0, 10)", vars) == 0.0
assert parse_and_visit("lerp(0, 10, 0.5)", vars) == 5.0
assert parse_and_visit("fract(1.25)", vars) == 0.25
# Type check - ensure they are python float/int, not tensor
res = parse_and_visit("a + b", vars)
assert isinstance(res, (float, int))
def test_tensor_ops():
t1 = torch.tensor([1.0, 2.0])
t2 = torch.tensor([3.0, 4.0])
vars = {"t1": t1, "t2": t2, "s": 2.0}
# Tensor + Tensor
res = parse_and_visit("t1 + t2", vars)
assert isinstance(res, torch.Tensor)
assert torch.allclose(res, torch.tensor([4.0, 6.0]))
# Tensor + Scalar
res2 = parse_and_visit("t1 * s", vars)
assert isinstance(res2, torch.Tensor)
assert torch.allclose(res2, torch.tensor([2.0, 4.0]))
# Scalar + Tensor
res3 = parse_and_visit("s + t2", vars)
assert isinstance(res3, torch.Tensor)
assert torch.allclose(res3, torch.tensor([5.0, 6.0]))
def test_list_broadcasting():
# Feature: List * Tensor -> Stack of Tensors
t = torch.ones((2, 2)) # 2x2 ones
l = [1.0, 2.0, 3.0]
vars = {"t": t, "l": l}
# l * t should produce a concatenation of 3 tensors: 1*t, 2*t, 3*t
# Expected shape with torch.cat: (6, 2) - concatenates along existing dim
res = parse_and_visit("l * t", vars)
assert isinstance(res, torch.Tensor)
assert res.shape == (6, 2)
# With torch.cat along dim=0, the result is flattened:
# [1*t[0], 1*t[1], 2*t[0], 2*t[1], 3*t[0], 3*t[1]]
assert torch.allclose(res[0:2], t * 1.0)
assert torch.allclose(res[2:4], t * 2.0)
assert torch.allclose(res[4:6], t * 3.0)
def test_list_scalar_mapping():
# Feature: List * Scalar -> List of results
l = [1.0, 2.0, 3.0]
vars = {"l": l}
res = parse_and_visit("l * 2", vars)
assert isinstance(res, list)
assert res == [2.0, 4.0, 6.0]
def test_func_dispatch():
t = torch.tensor([0.0, math.pi / 2])
vars = {"t": t, "s": 0.0}
# sin(tensor) -> tensor
res_t = parse_and_visit("sin(t)", vars)
assert isinstance(res_t, torch.Tensor)
assert torch.allclose(res_t, torch.tensor([0.0, 1.0]))
# sin(scalar) -> scalar
res_s = parse_and_visit("sin(s)", vars)
assert isinstance(res_s, float)
assert abs(res_s) < 1e-6
# sin(list) -> list
l = [0.0, math.pi / 2]
vars["l"] = l
res_l = parse_and_visit("sin(l)", vars)
assert isinstance(res_l, list)
assert abs(res_l[0]) < 1e-6
assert abs(res_l[1] - 1.0) < 1e-6
def test_power_ops():
vars = {"a": 2.0, "b": 3.0}
# Scalar ^ Scalar
assert parse_and_visit("a ^ b", vars) == 8.0
# Tensor ^ Scalar
t = torch.tensor([2.0, 3.0])
vars["t"] = t
res = parse_and_visit("t ^ 2", vars)
assert torch.allclose(res, torch.tensor([4.0, 9.0]))
# Scalar ^ Tensor
res2 = parse_and_visit("2 ^ t", vars)
assert torch.allclose(res2, torch.tensor([4.0, 8.0]))
def test_hyperbolic_trig():
vars = {"s": 0.0}
assert parse_and_visit("sinh(s)", vars) == 0.0
assert parse_and_visit("cosh(s)", vars) == 1.0
assert parse_and_visit("tanh(s)", vars) == 0.0
t = torch.tensor([0.0])
vars["t"] = t
res_sinh = parse_and_visit("sinh(t)", vars)
if isinstance(res_sinh, float): res_sinh = torch.tensor([res_sinh])
assert torch.allclose(res_sinh, torch.tensor([0.0]))
res_cosh = parse_and_visit("cosh(t)", vars)
if isinstance(res_cosh, float): res_cosh = torch.tensor([res_cosh])
assert torch.allclose(res_cosh, torch.tensor([1.0]))
def test_kernel_coords():
# Simulate visitConvFunc context
# Usually grid variables are provided by the visitor during visitConvFunc
# We can test if they are correctly handled if present in variables
grid = torch.linspace(-1, 1, 3)
vars = {"kx": grid, "ky": grid}
# Test if expression using coordinates works
res = parse_and_visit("kx^2 + ky^2", vars)
assert isinstance(res, torch.Tensor)
assert res.shape == grid.shape
assert torch.allclose(res, grid**2 + grid**2)
def test_bool_ops():
vars = {"a": 1, "b": 0}
# Scalar bool
assert parse_and_visit("a > b", vars) == 1
assert parse_and_visit("a < b", vars) == 0
# Tensor bool
t1 = torch.tensor([1.0, 0.0])
t2 = torch.tensor([0.0, 1.0])
vars = {"t1": t1, "t2": t2}
res = parse_and_visit("t1 > t2", vars)
assert isinstance(res, torch.Tensor)
assert torch.all(res == torch.tensor([1.0, 0.0]))
def test_topk():
# Tensor topk masking
t = torch.tensor([1.0, 5.0, 2.0, 8.0, 3.0])
vars = {"t": t}
res = parse_and_visit("topk(t, 3)", vars)
assert isinstance(res, torch.Tensor)
assert res.shape == t.shape
# Top 3 are 8, 5, 3. Masked result should be [0, 5, 0, 8, 3]
expected = torch.tensor([0.0, 5.0, 0.0, 8.0, 3.0])
assert torch.allclose(res, expected)
assert res.is_contiguous()
# Complex topk masking
tc = torch.tensor([1.0+1j, 5.0+5j, 2.0+2j])
vars["tc"] = tc
res_c = parse_and_visit("topk(tc, 1)", vars)
assert res_c.shape == tc.shape
# Top 1 is 5+5j. Masked should be [0, 5+5j, 0]
expected_c = torch.tensor([0.0+0j, 5.0+5j, 0.0+0j])
assert torch.allclose(res_c, expected_c)
def test_botk():
# Tensor botk masking (bottom k smallest values)
t = torch.tensor([1.0, 5.0, 2.0, 8.0, 3.0])
vars = {"t": t}
res = parse_and_visit("botk(t, 3)", vars)
assert isinstance(res, torch.Tensor)
assert res.shape == t.shape
# Bottom 3 are 1, 2, 3. Masked result should be [1, 0, 2, 0, 3]
expected = torch.tensor([1.0, 0.0, 2.0, 0.0, 3.0])
assert torch.allclose(res, expected)
assert res.is_contiguous()
# List botk
l = [1.0, 5.0, 2.0, 8.0, 3.0]
vars = {"l": l}
res_l = parse_and_visit("botk(l, 2)", vars)
assert isinstance(res_l, list)
assert res_l == [1.0, 2.0]
def test_pinv():
# List permutation inverse
p = [2, 0, 1] # 0->2, 1->0, 2->1
vars = {"p": p}
res = parse_and_visit("pinv(p)", vars)
assert isinstance(res, list)
# Inverse: if perm[i]=j, then inv[j]=i
# perm[0]=2 -> inv[2]=0
# perm[1]=0 -> inv[0]=1
# perm[2]=1 -> inv[1]=2
assert res == [1, 2, 0]
# Tensor permutation inverse
pt = torch.tensor([2, 0, 1])
vars["pt"] = pt
res_t = parse_and_visit("pinv(pt)", vars)
assert isinstance(res_t, torch.Tensor)
assert torch.equal(res_t, torch.tensor([1, 2, 0]))
def test_pinv_identity():
perm = [2,0,1,6,4,3,5]
tensor = torch.rand([11,14,32,21,4,3,1])
varbl = {'c':tensor,'a':perm}
res = parse_and_visit("permute(permute(c,a),pinv(a))",varbl)
assert torch.equal(tensor,res)
def test_quartil():
# Test Quartiles (Strict Integer Indices)
# List: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] (11 elements)
l = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
vars = {"l": l}
assert parse_and_visit("quartile(l, 0)", vars) == 0.0
assert parse_and_visit("quartile(l, 1)", vars) == 2.5
assert parse_and_visit("quartile(l, 2)", vars) == 5.0
assert parse_and_visit("quartile(l, 3)", vars) == 7.5
assert parse_and_visit("quartile(l, 4)", vars) == 10.0
# Tensor
t = torch.tensor(l, dtype=torch.float32)
vars["t"] = t
res_q2 = parse_and_visit("quartile(t, 2)", vars)
if not isinstance(res_q2, torch.Tensor): res_q2 = torch.tensor(res_q2)
assert torch.allclose(res_q2, torch.tensor(5.0))
res_q1 = parse_and_visit("quartile(t, 1)", vars)
if not isinstance(res_q1, torch.Tensor): res_q1 = torch.tensor(res_q1)
assert torch.allclose(res_q1, torch.tensor(2.5))
# Float inputs for quartil should be cast to int, so 0.5 -> 0 -> Min
# verifying strict behavior or fallback
assert parse_and_visit("quartile(l, 0.9)", vars) == 0.0
def test_percentile():
l = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
vars = {"l": l}
t = torch.tensor(l, dtype=torch.float32)
vars["t"] = t
# Percentile (0 - 100)
assert parse_and_visit("percentile(l, 50)", vars) == 5.0 # Median
assert parse_and_visit("percentile(l, 25)", vars) == 2.5 # Q1
res_p75 = parse_and_visit("percentile(t, 75)", vars)
if not isinstance(res_p75, torch.Tensor): res_p75 = torch.tensor(res_p75)
assert torch.allclose(res_p75, torch.tensor(7.5))
# Aliases
assert parse_and_visit("prcnt(l, 50)", vars) == 5.0
def test_custom_functions():
vars = {}
# 1. Simple function definition and call
# f(x) -> x + 1; f(10)
expr1 = "f(x) -> x + 1; f(10)"
assert parse_and_visit(expr1, vars) == 11.0
# 2. Multi-arg function
# add(a, b) -> a + b; add(3, 4)
expr2 = "add(a, b) -> a + b; add(3, 4)"
assert parse_and_visit(expr2, vars) == 7.0
# 3. Function reusing other functions (simulated by defining in same block)
# sq(x) -> x * x; sumsq(a, b) -> sq(a) + sq(b); sumsq(3, 4)
expr3 = "sq(x) -> x * x; sumsq(a, b) -> sq(a) + sq(b); sumsq(3, 4)"
assert parse_and_visit(expr3, vars) == 25.0
# 4. Global variable access & Shadowing
# x = 10 (global)
# f(x) -> x * 2; f(5) -> should be 10, not 20
vars = {"x": 10.0}
expr4 = "f(x) -> x * 2; f(5)"
assert parse_and_visit(expr4, vars) == 10.0
# f(y) -> x + y; f(5) -> global x(10) + param y(5) = 15
expr5 = "f(y) -> x + y; f(5)"
assert parse_and_visit(expr5, vars) == 15.0
def test_append():
vars = {}
# 1. Append scalar to list
assert parse_and_visit("append([1, 2], 3)", vars) == [1.0, 2.0, 3.0]
# 2. Append list to list (concat)
assert parse_and_visit("append([1], [2, 3])", vars) == [1.0, 2.0, 3.0]
# 3. Scalar a, list b
assert parse_and_visit("append(0, [1, 2])", vars) == [0.0, 1.0, 2.0]
# 4. Tensors
t1 = torch.tensor([1.0, 2.0])
t2 = torch.tensor([3.0])
vars = {"t1": t1, "t2": t2}
res = parse_and_visit("append(t1, t2)", vars)
assert torch.equal(res, torch.tensor([1.0, 2.0, 3.0]))
# 5. Tensor and scalar (promoted)
res2 = parse_and_visit("append(t1, 4)", vars)
assert torch.equal(res2, torch.tensor([1.0, 2.0, 4.0]))
def test_random_generators():
# We pass a shape to have non-scalar results
vars = {}
# Use parse_and_visit to test full stack
# 1. randn / noise (Normal distribution)
res_n = parse_and_visit("randn(123)", vars)
assert isinstance(res_n, torch.Tensor)
assert res_n.shape == (1, 1, 1, 1) # Default shape from parse_and_visit
res_noise = parse_and_visit("noise(123)", vars)
assert torch.equal(res_n, res_noise) # Same seed should give same results
# 2. rand (Uniform distribution [0, 1))
res_u = parse_and_visit("rand(123)", vars)
assert res_u.shape == (1, 1, 1, 1)
assert torch.all(res_u >= 0) and torch.all(res_u < 1)
# 3. rande / exponential
res_e = parse_and_visit("rande(123, 1.0)", vars)
assert res_e.shape == (1, 1, 1, 1)
assert torch.all(res_e >= 0)
res_exp = parse_and_visit("random_exponential(123, 1.0)", vars)
assert torch.equal(res_e, res_exp)
# 4. randc / cauchy
res_c = parse_and_visit("randc(123, 0.0, 1.0)", vars)
assert res_c.shape == (1, 1, 1, 1)
res_cauchy = parse_and_visit("random_cauchy(123, 0.0, 1.0)", vars)
assert torch.equal(res_c, res_cauchy)
# 5. randln / log_normal
res_ln = parse_and_visit("randln(123, 0.0, 1.0)", vars)
assert res_ln.shape == (1, 1, 1, 1)
assert torch.all(res_ln > 0)
res_lognorm = parse_and_visit("random_log_normal(123, 0.0, 1.0)", vars)
assert torch.equal(res_ln, res_lognorm)
# 6. randb / bernoulli
res_b = parse_and_visit("randb(123, 0.5)", vars)
assert res_b.shape == (1, 1, 1, 1)
assert torch.all((res_b == 0) | (res_b == 1))
# 7. randp / poisson
res_p = parse_and_visit("randp(123, 5.0)", vars)
assert res_p.shape == (1, 1, 1, 1)
assert torch.all(res_p >= 0)
def test_random_generators_with_list_shape():
vars = {}
res_rand = parse_and_visit("rand(123, [2, 3])", vars)
assert isinstance(res_rand, torch.Tensor)
assert res_rand.shape == (2, 3)
res_bernoulli = parse_and_visit("randb(123, 0.5, [2, 3])", vars)
assert isinstance(res_bernoulli, torch.Tensor)
assert res_bernoulli.shape == (2, 3)
assert torch.all((res_bernoulli == 0) | (res_bernoulli == 1))
res_poisson = parse_and_visit("randp(123, 5.0, [2, 3])", vars)
assert isinstance(res_poisson, torch.Tensor)
assert res_poisson.shape == (2, 3)
assert torch.all(res_poisson >= 0)
def test_recursion_and_depth():
vars = {}
# 1. Test recursion depth (100 levels)
# f(i) -> i == 0 ? 0 : f(i-1) + 1; f(100)
expr_rec = "f(i) -> i == 0 ? 0 : f(i-1) + 1; f(100)"
assert parse_and_visit(expr_rec, vars) == 100.0
# 2. Test 'depth' variable
# g(i) -> i == 0 ? depth : g(i-1); g(10)
# g(10) is depth 1, g(0) is depth 11
expr_depth = "g(i) -> i == 0 ? depth : g(i-1); g(10)"
assert parse_and_visit(expr_depth, vars) == 11.0
# 3. Test scope stack (shadowing with recursion)
# x = 100
# h(x, i) -> i == 0 ? x : h(x + 1, i - 1); h(0, 5)
# Should result in 5, not affected by global x=100 or previous levels' x in a broken way
vars = {"x": 100.0}
expr_scope = "h(x, i) -> i == 0 ? x : h(x + 1, i - 1); h(0, 5)"
assert parse_and_visit(expr_scope, vars) == 5.0
def test_new_loop_features():
vars = {}
# 1. Test FOR loop with range() (list)
# x = 0; for(i in range(0, 5, 1)) x = x + i; x
expr1 = "x = 0; for(i in range(0, 5, 1)) x = x + i; x"
assert parse_and_visit(expr1, vars) == 10.0
# 2. Test FOR loop with list
# x = 1; for(i in [1, 2, 3]) x = x * i; x
expr2 = "x = 1; for(i in [1, 2, 3]) x = x * i; x"
assert parse_and_visit(expr2, vars) == 6.0
# 3. Test get_value (2D tensor)
# T = [[1, 2], [3, 4]] -> pos=[1, 0] -> 3
t = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
vars = {"T": t}
# get_value(T, [1, 0])
expr3 = "get_value(T, [1, 0])"
res3 = parse_and_visit(expr3, vars)
assert res3 == 3.0
# get_value(T, [0, 1]) -> 2
assert parse_and_visit("get_value(T, [0, 1])", vars) == 2.0
# 4. Test crop
# crop(T, [0, 0], [1, 1]) -> [[1]]
res4 = parse_and_visit("crop(T, [0, 0], [1, 1])", vars)
assert torch.equal(res4, torch.tensor([[1.0]]))
# crop(T, [0, 0], [2, 2]) -> T
res5 = parse_and_visit("crop(T, [0, 0], [2, 2])", vars)
assert torch.equal(res5, t)
# 5. Test crop with padding (zeros)
# crop(T, [1, 1], [2, 2]) -> [[4, 0], [0, 0]]
# T at [1,1] is 4. Size [2,2].
# Row 1 (from T[1]): [4, T[1,2](out)] -> [4, 0]
# Row 2 (from T[2]): [0, 0]
expected_pad = torch.tensor([[4.0, 0.0], [0.0, 0.0]])
res6 = parse_and_visit("crop(T, [1, 1], [2, 2])", vars)
assert torch.equal(res6, expected_pad)
def test_entropy():
"""Test entropy function for information entropy calculation."""
vars = {}
# 1. Test uniform distribution (maximum entropy)
# Uniform probabilities should have high entropy
uniform = torch.ones(4) / 4.0 # [0.25, 0.25, 0.25, 0.25]
vars["uniform"] = uniform
entropy_uniform = parse_and_visit("entropy(uniform)", vars)
assert isinstance(entropy_uniform, float)
# For uniform distribution of 4 elements: H = -sum(0.25 * log(0.25)) = log(4) ≈ 1.386
assert 1.3 < entropy_uniform < 1.5
# 2. Test deterministic distribution (minimum entropy)
# One probability is 1, others are 0 -> entropy should be near 0
deterministic = torch.tensor([1000.0, -1000.0, -1000.0, -1000.0]) # After softmax: ~[1, 0, 0, 0]
vars["deterministic"] = deterministic
entropy_det = parse_and_visit("entropy(deterministic)", vars)
assert isinstance(entropy_det, float)
assert entropy_det < 0.1 # Near zero entropy
# 3. Test with different tensor sizes
small = torch.randn(8)
vars["small"] = small
entropy_small = parse_and_visit("entropy(small)", vars)
assert isinstance(entropy_small, float)
assert entropy_small > 0 # Should be positive
# 4. Test that entropy is always non-negative
random_vals = torch.randn(100)
vars["random_vals"] = random_vals
entropy_random = parse_and_visit("entropy(random_vals)", vars)
assert entropy_random >= 0
def test_correlation():
"""Test correlation (Pearson correlation coefficient) function."""
vars = {}
# 1. Perfect positive correlation
x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
y = torch.tensor([2.0, 4.0, 6.0, 8.0, 10.0]) # y = 2*x
vars["x"] = x
vars["y"] = y
corr_perfect = parse_and_visit("corr(x, y)", vars)
assert isinstance(corr_perfect, float)
assert abs(corr_perfect - 1.0) < 1e-5 # Should be very close to 1
# Test with alias
corr_alias = parse_and_visit("correlation(x, y)", vars)
assert abs(corr_alias - 1.0) < 1e-5
# 2. Perfect negative correlation
z = torch.tensor([10.0, 8.0, 6.0, 4.0, 2.0]) # Decreasing
vars["z"] = z
corr_negative = parse_and_visit("corr(x, z)", vars)
assert isinstance(corr_negative, float)
assert abs(corr_negative - (-1.0)) < 1e-5 # Should be very close to -1
# 3. No correlation (orthogonal)
a = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
b = torch.tensor([1.0, -1.0, 1.0, -1.0, 1.0]) # Oscillating
vars["a"] = a
vars["b"] = b
corr_none = parse_and_visit("corr(a, b)", vars)
assert isinstance(corr_none, float)
assert abs(corr_none) < 0.5 # Low correlation
# 4. Test with same tensor (should be 1.0)
corr_self = parse_and_visit("corr(x, x)", vars)
assert abs(corr_self - 1.0) < 1e-5
# 5. Test with flattened 2D tensors
t1 = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
t2 = torch.tensor([[1.5, 3.0], [4.5, 6.0]]) # Scaled version
vars["t1"] = t1
vars["t2"] = t2
corr_2d = parse_and_visit("corr(t1, t2)", vars)
assert isinstance(corr_2d, float)
assert abs(corr_2d - 1.0) < 1e-5 # Linear relationship
# 6. Test correlation is symmetric
corr_xy = parse_and_visit("corr(x, y)", vars)
corr_yx = parse_and_visit("corr(y, x)", vars)
assert abs(corr_xy - corr_yx) < 1e-10 # Should be identical
def test_entropy_and_correlation_edge_cases():
"""Test edge cases for entropy and correlation."""
vars = {}
# 1. Entropy with constant values (after softmax becomes uniform)
constant = torch.ones(10)
vars["constant"] = constant
entropy_const = parse_and_visit("entropy(constant)", vars)
# All equal logits -> uniform distribution after softmax -> log(10) ≈ 2.302
assert 2.2 < entropy_const < 2.4
# 2. Correlation with constant values (undefined, but should handle gracefully)
const_a = torch.ones(5) * 3.0
const_b = torch.ones(5) * 5.0
vars["const_a"] = const_a
vars["const_b"] = const_b
# Both have zero variance, correlation is undefined (0/0)
# Implementation returns nan or 0
corr_const = parse_and_visit("corr(const_a, const_b)", vars)
# Check that it doesn't crash and returns a float
assert isinstance(corr_const, float)
# 3. Small tensors
tiny_x = torch.tensor([1.0, 2.0])
tiny_y = torch.tensor([2.0, 4.0])
vars["tiny_x"] = tiny_x
vars["tiny_y"] = tiny_y
corr_tiny = parse_and_visit("corr(tiny_x, tiny_y)", vars)
assert abs(corr_tiny - 1.0) < 1e-5
if __name__ == "__main__":
try:
test_scalar_ops()
test_tensor_ops()
test_list_broadcasting()
test_list_scalar_mapping()
test_func_dispatch()
test_power_ops()
test_hyperbolic_trig()
test_kernel_coords()
test_bool_ops()
test_topk()
test_botk()
test_pinv()
test_pinv()
test_quartil()
test_percentile()
test_custom_functions()
test_append()
test_random_generators()
test_recursion_and_depth()
test_new_loop_features()
test_entropy()
test_correlation()
test_entropy_and_correlation_edge_cases()
print("All UnifiedMathVisitor tests passed!")
except Exception:
import traceback
traceback.print_exc()
sys.exit(1)
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