Commit bf075a4c authored by Jonas Boysen's avatar Jonas Boysen
Browse files

remove solution of optional programming tasks

parent 9be0d683

AgRobotSwarmForOptimization/field_optimization_filled_in.ipynb

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%% Cell type:code id: tags:



``` python

%matplotlib ipympl

import numpy as np

from field_optimization import Simulation, FieldVisualizer, IPyUI



def random_search_successor(current_state: list, neighbor_states: list[list], neighbor_costs: list) -> [list, bool]:

    terminate = False

    neighbor = neighbor_states.pop()

    neighbor_cost = neighbor_costs.pop()

    current_cost = optimize_sim.evaluate_state(state=current_state)

    if current_cost > neighbor_cost:

        return neighbor, terminate

    else:

        return current_state, terminate



def stochastic_hc_successor(current_state: list, neighbor_states: list[list], neighbor_costs: list) -> [list, bool]:

    current_cost = optimize_sim.evaluate_state(current_state)

    neighbor_cost_deltas = [current_cost - cost for cost in neighbor_costs]

    candidates = []

    weights = []

    for neighbor_state, neighbor_cost_delta in zip(neighbor_states, neighbor_cost_deltas):

        if neighbor_cost_delta > 0:

            candidates.append(neighbor_state)

            weights.append(neighbor_cost_delta)



    if not candidates:

        return current_state, True

    successor = np.random.choice(np.array(candidates), (1, len(candidates[0])), p=np.array(weights)).tolist()



    return successor, False



def t_schedule(t: int) -> float:

    t_max = 100

    return t_max - t * t



MAX_ITERATIONS = 50



optimize_sim = Simulation(field_save_dir="field", t_schedule=t_schedule, stochastic_hc=stochastic_hc_successor, random_search=random_search_successor)

optimize_sim.prepare_complete_state_representation()

field_vis = FieldVisualizer(optimize_sim, canvas_size=600)

ui = IPyUI(field_vis=field_vis, optimize_sim=optimize_sim, max_iterations=MAX_ITERATIONS)

ui.optimization_ui()

```

AgRobotSwarmForOptimization/graph_optimization_filled_in.ipynb

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%% Cell type:code id: tags:



``` python

%matplotlib ipympl

import numpy as np

from graph_optimization import Simulation, Visualizer, IPyUI



def random_search_successor(current_state: list, neighbor_states: list[list], neighbor_costs: list) -> [list, bool]:

    terminate = False

    neighbor = neighbor_states.pop()

    neighbor_cost = neighbor_costs.pop()

    current_cost = optimize_sim.evaluate_state(state=current_state)

    if current_cost > neighbor_cost:

        return neighbor, terminate

    else:

        return current_state, terminate



def stochastic_hc_successor(current_state: list, neighbor_states: list[list], neighbor_costs: list) -> [list, bool]:

    current_cost = optimize_sim.evaluate_state(current_state)

    neighbor_cost_deltas = [current_cost - cost for cost in neighbor_costs]

    candidates = []

    weights = []

    for neighbor_state, neighbor_cost_delta in zip(neighbor_states, neighbor_cost_deltas):

        if neighbor_cost_delta > 0:

            candidates.append(neighbor_state)

            weights.append(neighbor_cost_delta)



    if not candidates:

        return current_state, True

    successor = np.random.choice(np.array(candidates), (1, len(candidates[0])), p=np.array(weights)).tolist()



    return successor, False



def t_schedule(t: int) -> float:

    t_max = 100

    return t_max - t



MAX_ITERATIONS = 50



optimize_sim = Simulation(graph_load_dir="graph", t_schedule=t_schedule, stochastic_hc=stochastic_hc_successor, random_search=random_search_successor)

field_vis = Visualizer(optimize_sim, canvas_size=550)

ui = IPyUI(field_vis=field_vis, optimize_sim=optimize_sim, max_iterations=MAX_ITERATIONS)

ui.optimization_ui()

```