def init_julia(self):
print("Waiting for Julia to compile...")
j = Julia()
j.include(
os.path.join(os.path.dirname(__file__), 'n-dof', 'dynamics.jl'))
j.using("Dynamics")
return j
def solve_it(input_data):
"""
Run appropriate jl script to generate an integer.
"""
jl = Julia()
jl.include("any_integer.jl")
result = jl.eval(f'generate_any_integer("{input_data}")')
return result
def solve_it(input_data):
"""
Run appropriate jl script to solve the
knapsack problem in Julia.
"""
jl = Julia()
jl.include("knapsack.jl")
output_data = jl.eval(f'optimise_knapsack("{input_data}", timeout=60)')
return output_data
"""
Compute ranks of features and perform feature selection
Args:
data : Array[np.float64] -- matrix of examples on which to perform feature selection
target : Array[np.int] -- vector of target values of examples
Returns:
Array[np.float64] -- result of performing feature selection
"""
self.fit(data, target) # Fit data
return self.transform(data) # Perform feature selection
if __name__ == '__main__':
import scipy.io as sio
data = sio.loadmat('../datasets/final6/ovariancancer/data.mat')['data']
target = np.ravel(
sio.loadmat('../datasets/final6/ovariancancer/target.mat')['target'])
script_path = os.path.abspath(__file__)
get_dist_func = jl.include(script_path[:script_path.rfind('/')] +
"/augmentations/me_dissim2.jl")
dist_func = get_dist_func(10, data)
import pdb
pdb.set_trace()
relief = Relief(dist_func=dist_func)
relief.fit(data, target)
print(relief.weights)
