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Fix description #166

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8d019f2
Fix description
xiaokongkong May 11, 2024
55f4b39
remove extra symbols
xiaokongkong May 11, 2024
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8 changes: 4 additions & 4 deletions docs/Examples/Example_4_symbolic_regression.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@
],
"source": [
"# train the model\n",
"model.train(dataset, opt=\"LBFGS\", steps=20, lamb=0.01, lamb_entropy=10.);"
"model.train(dataset, opt=\"LBFGS\", steps=20, lamb=0.01, lamb_entropy=10.)"
]
},
{
Expand Down Expand Up @@ -286,7 +286,7 @@
}
],
"source": [
"model.train(dataset, opt=\"LBFGS\", steps=20);\n",
"model.train(dataset, opt=\"LBFGS\", steps=20)\n",
"model.plot()"
]
},
Expand Down Expand Up @@ -465,7 +465,7 @@
}
],
"source": [
"model.train(dataset, opt=\"LBFGS\", steps=20);\n",
"model.train(dataset, opt=\"LBFGS\", steps=20)\n",
"model.plot()"
]
},
Expand Down Expand Up @@ -612,7 +612,7 @@
],
"source": [
"# this loss is stuck at around 1e-3 RMSE, which is good, but not machine precision.\n",
"model.train(dataset, opt=\"LBFGS\", steps=20);\n",
"model.train(dataset, opt=\"LBFGS\", steps=20)\n",
"model.plot()"
]
},
Expand Down
14 changes: 7 additions & 7 deletions hellokan.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -180,7 +180,7 @@
],
"source": [
"# plot KAN at initialization\n",
"model(dataset['train_input']);\n",
"model(dataset['train_input'])\n",
"model.plot(beta=100)"
]
},
Expand Down Expand Up @@ -208,7 +208,7 @@
],
"source": [
"# train the model\n",
"model.train(dataset, opt=\"LBFGS\", steps=20, lamb=0.01, lamb_entropy=10.);"
"model.train(dataset, opt=\"LBFGS\", steps=20, lamb=0.01, lamb_entropy=10.)"
]
},
{
Expand Down Expand Up @@ -324,7 +324,7 @@
}
],
"source": [
"model.train(dataset, opt=\"LBFGS\", steps=50);"
"model.train(dataset, opt=\"LBFGS\", steps=50)"
]
},
{
Expand Down Expand Up @@ -377,9 +377,9 @@
"\n",
"if mode == \"manual\":\n",
" # manual mode\n",
" model.fix_symbolic(0,0,0,'sin');\n",
" model.fix_symbolic(0,1,0,'x^2');\n",
" model.fix_symbolic(1,0,0,'exp');\n",
" model.fix_symbolic(0,0,0,'sin')\n",
" model.fix_symbolic(0,1,0,'x^2')\n",
" model.fix_symbolic(1,0,0,'exp')\n",
"elif mode == \"auto\":\n",
" # automatic mode\n",
" lib = ['x','x^2','x^3','x^4','exp','log','sqrt','tanh','sin','abs']\n",
Expand Down Expand Up @@ -409,7 +409,7 @@
}
],
"source": [
"model.train(dataset, opt=\"LBFGS\", steps=50);"
"model.train(dataset, opt=\"LBFGS\", steps=50)"
]
},
{
Expand Down
38 changes: 19 additions & 19 deletions kan/KAN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ class KAN(nn.Module):
depth: int
depth of KAN
width: list
number of neurons in each layer. e.g., [2,5,5,3] means 2D inputs, 5D outputs, with 2 layers of 5 hidden neurons.
number of neurons in each layer. e.g., [2,5,5,3] means 2D inputs, 3D outputs, with 2 layers of 5 hidden neurons.
grid: int
the number of grid intervals
k: int
Expand Down Expand Up @@ -185,7 +185,7 @@ def initialize_from_another_model(self, another_model, x):
>>> model_fine = KAN(width=[2,5,1], grid=10, k=3)
>>> print(model_fine.act_fun[0].coef[0][0].data)
>>> x = torch.normal(0,1,size=(100,2))
>>> model_fine.initialize_from_another_model(model_coarse, x);
>>> model_fine.initialize_from_another_model(model_coarse, x)
>>> print(model_fine.act_fun[0].coef[0][0].data)
tensor(-0.0030)
tensor(0.0506)
Expand Down Expand Up @@ -348,27 +348,27 @@ def set_mode(self, l, i, j, mode, mask_n=None):
output neuron index
mode : str
'n' (numeric) or 's' (symbolic) or 'ns' (combined)
mask_n : None or float)
mask_n : None or float
magnitude of the numeric front

Returns:
--------
None
'''
if mode == "s":
mask_n = 0.;
mask_n = 0.
mask_s = 1.
elif mode == "n":
mask_n = 1.;
mask_n = 1.
mask_s = 0.
elif mode == "sn" or mode == "ns":
if mask_n == None:
if mask_n is None:
mask_n = 1.
else:
mask_n = mask_n
mask_s = 1.
else:
mask_n = 0.;
mask_n = 0.
mask_s = 0.

self.act_fun[l].mask.data[j * self.act_fun[l].in_dim + i] = mask_n
Expand Down Expand Up @@ -814,7 +814,7 @@ def train(self, dataset, opt="LBFGS", steps=100, log=1, lamb=0., lamb_l1=1., lam
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model.plot()
'''

Expand Down Expand Up @@ -842,7 +842,7 @@ def nonlinear(x, th=small_mag_threshold, factor=small_reg_factor):

pbar = tqdm(range(steps), desc='description', ncols=100)

if loss_fn == None:
if loss_fn is None:
loss_fn = loss_fn_eval = lambda x, y: torch.mean((x - y) ** 2)
else:
loss_fn = loss_fn_eval = loss_fn
Expand Down Expand Up @@ -958,7 +958,7 @@ def prune(self, threshold=1e-2, mode="auto", active_neurons_id=None):
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model.prune()
>>> model.plot(mask=True)
'''
Expand Down Expand Up @@ -1063,7 +1063,7 @@ def suggest_symbolic(self, l, i, j, a_range=(-10, 10), b_range=(-10, 10), lib=No
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.suggest_symbolic(0,0,0)
Expand All @@ -1076,7 +1076,7 @@ def suggest_symbolic(self, l, i, j, a_range=(-10, 10), b_range=(-10, 10), lib=No
'''
r2s = []

if lib == None:
if lib is None:
symbolic_lib = SYMBOLIC_LIB
else:
symbolic_lib = {}
Expand Down Expand Up @@ -1124,8 +1124,8 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> >>> model = model.prune()
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.auto_symbolic()
fixing (0,0,0) with sin, r2=0.9994837045669556
Expand All @@ -1139,8 +1139,8 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> >>> model = model.prune()
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.auto_symbolic(lib=['exp','sin','x^2'])
fixing (0,0,0) with sin, r2=0.999411404132843
Expand Down Expand Up @@ -1184,11 +1184,11 @@ def symbolic_formula(self, floating_digit=2, var=None, normalizer=None, simplify
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0, grid_eps=0.02)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.auto_symbolic(lib=['exp','sin','x^2'])
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.00, update_grid=False);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.00, update_grid=False)
>>> model.symbolic_formula()
'''
symbolic_acts = []
Expand All @@ -1202,7 +1202,7 @@ def ex_round(ex1, floating_digit=floating_digit):
return ex2

# define variables
if var == None:
if var is None:
for ii in range(1, self.width[0] + 1):
exec(f"x{ii} = sympy.Symbol('x_{ii}')")
exec(f"x.append(x{ii})")
Expand Down