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fix bugs in to python code, see issue(https://github.com/EMI-Group/tensorneat/issues/24#issuecomment-2731438212)

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wls2002
2025-03-19 11:56:06 +08:00
parent 540f1b3a43
commit 55f1c626d3
2 changed files with 34 additions and 49 deletions
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64
src/tensorneat/common/sympy_tools.py
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@@ -22,70 +22,54 @@ def round_expr(expr, precision=2):
return expr.xreplace({n: round(n, precision) for n in expr.atoms(sp.Number)})
def replace_variable_names(expression):
def replace_variable_names(expression, mode):
"""
Transform sympy expression to a string with array index that can be used in python code.
For example, `o0` will be transformed to `o[0]`.
For example, `o0` will be transformed to `o[0]` in Python mode,
and `o0` will be transformed to LaTeX format using sympy's `latex()` in LaTeX mode.
"""
assert mode in ["python", "latex"]
expression_str = str(expression)
if mode == "python":
expression_str = re.sub(r"\bo(\d+)\b", r"o[\1]", expression_str)
expression_str = re.sub(r"\bh(\d+)\b", r"h[\1]", expression_str)
expression_str = re.sub(r"\bi(\d+)\b", r"i[\1]", expression_str)
else: # latex mode
expression_str = re.sub(r"\bo(\d+)\b", lambda m: f"o_{{{m.group(1)}}}", expression_str)
expression_str = re.sub(r"\bh(\d+)\b", lambda m: f"h_{{{m.group(1)}}}", expression_str)
expression_str = re.sub(r"\bi(\d+)\b", lambda m: f"i_{{{m.group(1)}}}", expression_str)
return expression_str
def to_latex_code(symbols, args_symbols, input_symbols, nodes_exprs, output_exprs, use_hidden_nodes=True):
def to_latex_code(symbols, args_symbols, input_symbols, nodes_exprs, output_exprs, forward_func, topo_order):
input_cnt, hidden_cnt, output_cnt, norm_symbols = analysis_nodes_exprs(nodes_exprs)
res = "\\begin{align}\n"
if not use_hidden_nodes:
for i in range(output_cnt):
expr = output_exprs[i].subs(args_symbols)
rounded_expr = round_expr(expr, 2)
latex_expr = f"o_{{{sp.latex(i)}}} &= {sp.latex(rounded_expr)}\\newline\n"
res += latex_expr
else:
for i in range(hidden_cnt):
symbol = sp.symbols(f"h{i}")
for i in topo_order[input_cnt: ]:
symbol = symbols[i]
expr = nodes_exprs[symbol].subs(args_symbols).subs(norm_symbols)
rounded_expr = round_expr(expr, 2)
latex_expr = f"h_{{{sp.latex(i)}}} &= {sp.latex(rounded_expr)}\\newline\n"
res += latex_expr
for i in range(output_cnt):
symbol = sp.symbols(f"o{i}")
expr = nodes_exprs[symbol].subs(args_symbols).subs(norm_symbols)
rounded_expr = round_expr(expr, 2)
latex_expr = f"o_{{{sp.latex(i)}}} &= {sp.latex(rounded_expr)}\\newline\n"
latex_expr = f"{symbol} &= {sp.latex(rounded_expr)}\\newline\n"
latex_expr = replace_variable_names(latex_expr, "latex")
res += latex_expr
res += "\\end{align}\n"
return res
def to_python_code(symbols, args_symbols, input_symbols, nodes_exprs, output_exprs, use_hidden_nodes=True):
def to_python_code(symbols, args_symbols, input_symbols, nodes_exprs, output_exprs, forward_func, topo_order):
input_cnt, hidden_cnt, output_cnt, norm_symbols = analysis_nodes_exprs(nodes_exprs)
res = ""
if not use_hidden_nodes:
# pre-allocate space
res += f"o = np.zeros({output_cnt})\n"
for i in range(output_cnt):
expr = output_exprs[i].subs(args_symbols)
rounded_expr = round_expr(expr, 6)
str_expr = f"o{i} = {rounded_expr}"
res += replace_variable_names(str_expr) + "\n"
else:
# pre-allocate space
if hidden_cnt > 0:
res += f"h = np.zeros({hidden_cnt})\n"
res += f"o = np.zeros({output_cnt})\n"
for i in range(hidden_cnt):
symbol = sp.symbols(f"h{i}")
for i in topo_order[input_cnt: ]:
symbol = symbols[i]
expr = nodes_exprs[symbol].subs(args_symbols).subs(norm_symbols)
rounded_expr = round_expr(expr, 6)
str_expr = f"h{i} = {rounded_expr}"
res += replace_variable_names(str_expr) + "\n"
for i in range(output_cnt):
symbol = sp.symbols(f"o{i}")
expr = nodes_exprs[symbol].subs(args_symbols).subs(norm_symbols)
rounded_expr = round_expr(expr, 6)
str_expr = f"o{i} = {rounded_expr}"
res += replace_variable_names(str_expr) + "\n"
str_expr = f"{symbol} = {rounded_expr}"
res += replace_variable_names(str_expr, "python") + "\n"
return res

1
src/tensorneat/genome/default.py
View File

@@ -251,6 +251,7 @@ class DefaultGenome(BaseGenome):
nodes_exprs,
output_exprs,
forward_func,
network["topo_order"]
)
def visualize(