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)