feat: Added numpy.random.noncentral_chisquare function to Frontend (i…

…vy-llc#23378)

Co-authored-by: Kareem Morsy <[email protected]>

ivy/functional/frontends/numpy/random/functions.py

@@ -5,6 +5,7 @@
     to_ivy_arrays_and_back,
     from_zero_dim_arrays_to_scalar,
 )
+from ivy import with_supported_dtypes
 
 
 @to_ivy_arrays_and_back
@@ -184,6 +185,27 @@ def negative_binomial(n, p, size=None):
     return ivy.poisson(lam=lambda_, shape=size)
 
 
+@with_supported_dtypes(
+    {"1.25.2 and below": ("float16", "float32")},
+    "numpy",
+)
+@to_ivy_arrays_and_back
+@from_zero_dim_arrays_to_scalar
+def noncentral_chisquare(df, nonc, size=None):
+    if ivy.any(df <= 0):
+        raise ValueError("Degree of freedom must be greater than 0")
+    if ivy.has_nans(nonc):
+        return ivy.nan
+    if ivy.any(nonc == 0):
+        return chisquare(df, size=size)
+    if ivy.any(df < 1):
+        n = standard_normal() + ivy.sqrt(nonc)
+        return chisquare(df - 1, size=size) + n * n
+    else:
+        i = poisson(nonc / 2.0, size=size)
+        return chisquare(df + 2 * i, size=size)
+
+
 @to_ivy_arrays_and_back
 @from_zero_dim_arrays_to_scalar
 def normal(loc=0.0, scale=1.0, size=None):

ivy_tests/test_ivy/test_frontends/test_numpy/test_random/test_functions.py

@@ -672,6 +672,47 @@ def test_numpy_negative_binomial(
     )
 
 
+# noncentral_chisquare
+@handle_frontend_test(
+    fn_tree="numpy.random.noncentral_chisquare",
+    dtype_and_df=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_value=0,
+        exclude_min=True,
+    ),
+    dtype_and_nonc=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_value=0,
+    ),
+    size=helpers.get_shape(allow_none=True),
+    test_with_out=st.just(False),
+)
+def test_numpy_noncentral_chisquare(
+    dtype_and_df,
+    dtype_and_nonc,
+    size,
+    frontend,
+    test_flags,
+    fn_tree,
+    backend_fw,
+    on_device,
+):
+    dtype_df, df = dtype_and_df
+    dtype_nonc, nonc = dtype_and_nonc
+    helpers.test_frontend_function(
+        input_dtypes=dtype_df + dtype_nonc,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=False,
+        df=df[0],
+        nonc=nonc[0],
+        size=size,
+    )
+
+
 # normal
 @handle_frontend_test(
     fn_tree="numpy.random.normal",