質問編集履歴

きれいに整形

2020/08/03 05:19

投稿

RyosukeSHIBATA

スコア11

test CHANGED Viewed

File without changes

test CHANGED Viewed

@@ -42,6 +42,374 @@
     for dish in ind:
+        axis.add_patch(dish.fig)#<-----ここでエラーが発生
+    axis.axis("off")
+    plt.savefig('tmp.png')
+    fig.delaxes(axis)
+    plt.clf()
+    plt.cla()
+    plt.close()
+    del axis
+    gray_img = cv2.imread('tmp.png')
+    ind.img = gray_img
+    ret, dishes_area_image = cv2.threshold(gray_img, 200, 255, cv2.THRESH_BINARY)#図形がある
+    dishes_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_area_image, cv2.COLOR_RGB2GRAY)))
+    cv2.imwrite('dishes_area.png',dishes_area_image)
+    ret, dishes_overlap_area_image = cv2.threshold(gray_img, 100, 255, cv2.THRESH_BINARY)#重なってて黒い
+    cv2.imwrite('dishes_overlap_area.png',dishes_overlap_area_image)
+    dishes_overlap_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_overlap_area_image, cv2.COLOR_RGB2GRAY)))
+    overlap_per = 1 - dishes_overlap_area/dishes_area
+    return evaluation * overlap_per
+```
+# プログラム全体
+```
+import random
+import numpy as np
+from operator import attrgetter
+import csv
+import matplotlib
+import matplotlib.pyplot as plt
+import cv2
+#
+def main():
+    n_gene = 4  # The number of genes.
+    n_ind = 300  # The number of individuals in a population.
+    CXPB = 0.5  # The probability of crossover.
+    MUTPB = 0.2  # The probability of individdual mutation.
+    MUTINDPB = 0.05  # The probability of gene mutation.
+    NGEN = 40  # The number of generation loop.
+    dish_types= DishTypes()
+    dish_types.load_file()
+    random.seed(64)
+    # --- Step1 : Create initial generation.
+    pop = create_pop(n_ind, n_gene, dish_types)
+    set_fitness(evalOneMax, pop)
+    best_ind = max(pop, key=attrgetter("fitness"))
+    # --- Generation loop.
+    print("Generation loop start.")
+    print("Generation: 0. Best fitness: " + str(best_ind.fitness))
+    fig_save(best_ind, '{:0=5}'.format(0))
+    for g in range(NGEN):
+        # --- Step2 : Selection.
+        offspring = selTournament(pop, n_ind, tournsize=3)
+        # --- Step3 : Crossover.
+        crossover = []
+        for child1, child2 in zip(offspring[::2], offspring[1::2]):# (偶数個目，奇数個目)で子孫を作成
+            if random.random() < CXPB:
+                child1, child2 = cxTwoPointCopy(child1, child2)
+                # child1.fitness = None
+                # child2.fitness = None
+            crossover.append(child1)
+            crossover.append(child2)
+        offspring = crossover[:]
+        # --- Step4 : Mutation.
+        mutant = []
+        for mut in offspring:
+            if random.random() < MUTPB:
+                mut = mutFlipBit(dish_types, mut, indpb=MUTINDPB)
+                # mut.fitness = None
+            mutant.append(mut)
+        offspring = mutant[:]
+        # --- Update next population.
+        pop = offspring[:]
+        set_fitness(evalOneMax, pop)
+        # --- Print best fitness in the population.
+        best_ind = max(pop, key=attrgetter("fitness"))
+        print("Generation: " + str(g + 1) + ". Best fitness: " + str(best_ind.fitness))
+        fig_save(best_ind, dish_types, '{:0=5}'.format(g + 1))
+    print("Generation loop ended. The best individual: ")
+    print(best_ind)
+class DishTypes:
+    def __init__(self):
+        self.num=0
+        self.type=[]
+        self.size=[]
+        self.name=[]
+        self.calorie=[]
+        self.protein=[]
+        self.lipid=[]
+        self.carbohydrate=[]
+        self.calcium=[]
+        self.price=[]
+        self.tray_size=np.array([370, 260])
+    def load_file(self):
+        with open('dish.csv') as f:
+            reader = csv.reader(f)
+            for row in reader:
+                self.name.append(row[0])
+                self.type.append(int(row[1]))
+                self.size.append(int(row[2]))
+                self.calorie.append(float(row[3]))
+                self.protein.append(float(row[4]))
+                self.lipid.append(float(row[5]))
+                self.carbohydrate.append(float(row[6]))
+                self.calcium.append(float(row[7]))
+                self.price.append(float(row[8]))
+        self.num = len(self.name)
+class Dish:
+    def __init__(self, id, dish_types):
+        self.figure_type=dish_types.type[id]
+        self.fig=create_figure(dish_types.type[id],dish_types.size[id],dish_types.tray_size)
+        self.name=dish_types.name[id]
+        self.calorie=dish_types.calorie[id]
+        self.protein=dish_types.protein[id]
+        self.lipid=dish_types.lipid[id]
+        self.carbohydrate=dish_types.carbohydrate[id]
+        self.calcium=dish_types.calcium[id]
+        self.price=dish_types.price[id]
+class IndividualTray(list):
+    """Container of a individual."""
+    fitness = None
+    img = None
+    def __new__(cls, a):
+        return list.__new__(cls, a)
+def create_figure(fig_type, size, tray_size):
+    x = random.randrange(size, tray_size[0] - size)
+    y = random.randrange(size, tray_size[1] - size)
+    if fig_type == 0:
+        return matplotlib.patches.Rectangle((x, y), size, size, facecolor='black', alpha=0.5)
+    if fig_type == 1:
+        return matplotlib.patches.Circle((x, y), radius=size, facecolor='black', alpha=0.5)
+def create_ind(n_gene, dish_types):
+    """Create a individual."""
+    return IndividualTray([Dish(random.randint(0, dish_types.num-1), dish_types) for i in range(n_gene)])
+def create_pop(n_ind, n_gene, dish_types):
+    """Create a population."""
+    pop = []
+    for i in range(n_ind):
+        ind = create_ind(n_gene, dish_types)
+        pop.append(ind)
+    return pop
+def set_fitness(eval_func, pop):
+    """Set fitnesses of each individual in a population."""
+    for i, fit in zip(range(len(pop)), map(eval_func, pop)):
+        pop[i].fitness = fit
+def evalOneMax(ind):
+    """Objective function."""
+    evaluation = 0
+    for dish in ind:
+        evaluation += dish.calorie
+    # TODO:グラフ領域を設定
+    #TODO:いい感じにトレーのサイズを外から引数として撮ってくるやり方がわからない直接書いた
+    tray_size = np.array([370, 260])
+    fig = plt.figure()
+    axis = fig.add_subplot(1, 1, 1, xlim=tray_size[0], ylim=tray_size[1], aspect='equal')
+    for dish in ind:
         axis.add_patch(dish.fig)
     axis.axis("off")
@@ -80,476 +448,108 @@
     return evaluation * overlap_per
+def selTournament(pop, n_ind, tournsize):
+    """Selection function."""
+    chosen = []
+    for i in range(n_ind):
+        aspirants = [random.choice(pop) for j in range(tournsize)]#適当にtournsizeこだけ個体を選ぶ
+        chosen.append(max(aspirants, key=attrgetter("fitness")))#fitness属性の一番大きいものを選ぶ
+    return chosen
+def cxTwoPointCopy(ind1, ind2):
+    """Crossover function."""
+    size1 = len(ind1)
+    size2 = len(ind2)
+    tmp1 = ind1.copy()
+    tmp2 = ind2.copy()
+    cxpoint1 = int(size1/2)
+    cxpoint2 = int(size2/2)
+    tmp1, tmp2 = tmp2[:cxpoint2].copy()+tmp1[cxpoint1:].copy(), tmp1[:cxpoint1].copy()+tmp2[cxpoint2:].copy()
+    return tmp1, tmp2
+def mutFlipBit(dish_types, ind, indpb):
+    """Mutation function."""
+    tmp = ind.copy()
+    for i in range(len(ind)):
+        if random.random() < indpb:
+            rand = random.randint(0, 2)
+            # 増やす
+            if rand == 0:
+                tmp[i] = ind.append(Dish(random.randint(0, dish_types.num-1), dish_types))
+                break
+            # へらす
+            if rand == 1:
+                tmp[i] = ind.pop(random.randint(0,len(ind)-1))
+                break
+            # 変える
+            if rand == 2:
+                randopos = random.randint(0, len(ind) - 1)
+                tmp[i] = ind.pop(randopos)
+                tmp[i] = ind.insert(randopos, random.randint(0, len(ind) - 1))
+                break
+    return tmp
+def fig_save(ind, file_name):
+    cv2.imwrite(',/img/'+file_name+'.png', ind.img)
+if __name__ == "__main__":
+    main()
 ```
-# プログラム全体
-```
-import random
-import numpy as np
-from operator import attrgetter
-import csv
-import matplotlib
-import matplotlib.pyplot as plt
-import cv2
-#
-def main():
-    n_gene = 4  # The number of genes.
-    n_ind = 300  # The number of individuals in a population.
-    CXPB = 0.5  # The probability of crossover.
-    MUTPB = 0.2  # The probability of individdual mutation.
-    MUTINDPB = 0.05  # The probability of gene mutation.
-    NGEN = 40  # The number of generation loop.
-    dish_types= DishTypes()
-    dish_types.load_file()
-    random.seed(64)
-    # --- Step1 : Create initial generation.
-    pop = create_pop(n_ind, n_gene, dish_types)
-    set_fitness(evalOneMax, pop)
-    best_ind = max(pop, key=attrgetter("fitness"))
-    # --- Generation loop.
-    print("Generation loop start.")
-    print("Generation: 0. Best fitness: " + str(best_ind.fitness))
-    fig_save(best_ind, '{:0=5}'.format(0))
-    for g in range(NGEN):
-        # --- Step2 : Selection.
-        offspring = selTournament(pop, n_ind, tournsize=3)
-        # --- Step3 : Crossover.
-        crossover = []
-        for child1, child2 in zip(offspring[::2], offspring[1::2]):# (偶数個目，奇数個目)で子孫を作成
-            if random.random() < CXPB:
-                child1, child2 = cxTwoPointCopy(child1, child2)
-                # child1.fitness = None
-                # child2.fitness = None
-            crossover.append(child1)
-            crossover.append(child2)
-        offspring = crossover[:]
-        # --- Step4 : Mutation.
-        mutant = []
-        for mut in offspring:
-            if random.random() < MUTPB:
-                mut = mutFlipBit(dish_types, mut, indpb=MUTINDPB)
-                # mut.fitness = None
-            mutant.append(mut)
-        offspring = mutant[:]
-        # --- Update next population.
-        pop = offspring[:]
-        set_fitness(evalOneMax, pop)
-        # --- Print best fitness in the population.
-        best_ind = max(pop, key=attrgetter("fitness"))
-        print("Generation: " + str(g + 1) + ". Best fitness: " + str(best_ind.fitness))
-        fig_save(best_ind, dish_types, '{:0=5}'.format(g + 1))
-    print("Generation loop ended. The best individual: ")
-    print(best_ind)
-class DishTypes:
-    def __init__(self):
-        self.num=0
-        self.type=[]
-        self.size=[]
-        self.name=[]
-        self.calorie=[]
-        self.protein=[]
-        self.lipid=[]
-        self.carbohydrate=[]
-        self.calcium=[]
-        self.price=[]
-        self.tray_size=np.array([370, 260])
-    def load_file(self):
-        with open('dish.csv') as f:
-            reader = csv.reader(f)
-            for row in reader:
-                self.name.append(row[0])
-                self.type.append(int(row[1]))
-                self.size.append(int(row[2]))
-                self.calorie.append(float(row[3]))
-                self.protein.append(float(row[4]))
-                self.lipid.append(float(row[5]))
-                self.carbohydrate.append(float(row[6]))
-                self.calcium.append(float(row[7]))
-                self.price.append(float(row[8]))
-        self.num = len(self.name)
-class Dish:
-    def __init__(self, id, dish_types):
-        self.figure_type=dish_types.type[id]
-        self.fig=create_figure(dish_types.type[id],dish_types.size[id],dish_types.tray_size)
-        self.name=dish_types.name[id]
-        self.calorie=dish_types.calorie[id]
-        self.protein=dish_types.protein[id]
-        self.lipid=dish_types.lipid[id]
-        self.carbohydrate=dish_types.carbohydrate[id]
-        self.calcium=dish_types.calcium[id]
-        self.price=dish_types.price[id]
-class IndividualTray(list):
-    """Container of a individual."""
-    fitness = None
-    img = None
-    def __new__(cls, a):
-        return list.__new__(cls, a)
-def create_figure(fig_type, size, tray_size):
-    x = random.randrange(size, tray_size[0] - size)
-    y = random.randrange(size, tray_size[1] - size)
-    if fig_type == 0:
-        return matplotlib.patches.Rectangle((x, y), size, size, facecolor='black', alpha=0.5)
-    if fig_type == 1:
-        return matplotlib.patches.Circle((x, y), radius=size, facecolor='black', alpha=0.5)
-def create_ind(n_gene, dish_types):
-    """Create a individual."""
-    return IndividualTray([Dish(random.randint(0, dish_types.num-1), dish_types) for i in range(n_gene)])
-def create_pop(n_ind, n_gene, dish_types):
-    """Create a population."""
-    pop = []
-    for i in range(n_ind):
-        ind = create_ind(n_gene, dish_types)
-        pop.append(ind)
-    return pop
-def set_fitness(eval_func, pop):
-    """Set fitnesses of each individual in a population."""
-    for i, fit in zip(range(len(pop)), map(eval_func, pop)):
-        pop[i].fitness = fit
-def evalOneMax(ind):
-    """Objective function."""
-    evaluation = 0
-    for dish in ind:
-        evaluation += dish.calorie
-    # TODO:グラフ領域を設定
-    #TODO:いい感じにトレーのサイズを外から引数として撮ってくるやり方がわからない直接書いた
-    tray_size = np.array([370, 260])
-    fig = plt.figure()
-    axis = fig.add_subplot(1, 1, 1, xlim=tray_size[0], ylim=tray_size[1], aspect='equal')
-    for dish in ind:
-        axis.add_patch(dish.fig)
-    axis.axis("off")
-    plt.savefig('tmp.png')
-    fig.delaxes(axis)
-    plt.clf()
-    plt.cla()
-    plt.close()
-    del axis
-    gray_img = cv2.imread('tmp.png')
-    ind.img = gray_img
-    ret, dishes_area_image = cv2.threshold(gray_img, 200, 255, cv2.THRESH_BINARY)#図形がある
-    dishes_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_area_image, cv2.COLOR_RGB2GRAY)))
-    cv2.imwrite('dishes_area.png',dishes_area_image)
-    ret, dishes_overlap_area_image = cv2.threshold(gray_img, 100, 255, cv2.THRESH_BINARY)#重なってて黒い
-    cv2.imwrite('dishes_overlap_area.png',dishes_overlap_area_image)
-    dishes_overlap_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_overlap_area_image, cv2.COLOR_RGB2GRAY)))
-    overlap_per = 1 - dishes_overlap_area/dishes_area
-    return evaluation * overlap_per
-def selTournament(pop, n_ind, tournsize):
-    """Selection function."""
-    chosen = []
-    for i in range(n_ind):
-        aspirants = [random.choice(pop) for j in range(tournsize)]#適当にtournsizeこだけ個体を選ぶ
-        chosen.append(max(aspirants, key=attrgetter("fitness")))#fitness属性の一番大きいものを選ぶ
-    return chosen
-def cxTwoPointCopy(ind1, ind2):
-    """Crossover function."""
-    size1 = len(ind1)
-    size2 = len(ind2)
-    tmp1 = ind1.copy()
-    tmp2 = ind2.copy()
-    cxpoint1 = int(size1/2)
-    cxpoint2 = int(size2/2)
-    tmp1, tmp2 = tmp2[:cxpoint2].copy()+tmp1[cxpoint1:].copy(), tmp1[:cxpoint1].copy()+tmp2[cxpoint2:].copy()
-    return tmp1, tmp2
-def mutFlipBit(dish_types, ind, indpb):
-    """Mutation function."""
-    tmp = ind.copy()
-    for i in range(len(ind)):
-        if random.random() < indpb:
-            rand = random.randint(0, 2)
-            # 増やす
-            if rand == 0:
-                tmp[i] = ind.append(Dish(random.randint(0, dish_types.num-1), dish_types))
-                break
-            # へらす
-            if rand == 1:
-                tmp[i] = ind.pop(random.randint(0,len(ind)-1))
-                break
-            # 変える
-            if rand == 2:
-                randopos = random.randint(0, len(ind) - 1)
-                tmp[i] = ind.pop(randopos)
-                tmp[i] = ind.insert(randopos, random.randint(0, len(ind) - 1))
-                break
-    return tmp
-def fig_save(ind, file_name):
-    cv2.imwrite(',/img/'+file_name+'.png', ind.img)
-if __name__ == "__main__":
-    main()
-```

きれいに整形

2020/08/03 05:19

投稿

RyosukeSHIBATA

スコア11

test CHANGED Viewed

File without changes

test CHANGED Viewed

@@ -6,7 +6,87 @@
 というエラーが起きて困っています．
+# 問題箇所
+https://stackoverrun.com/ja/q/12300089
+こちらのリンクでplt.figureを複数設定して解決されている話はあるのですが，
+今のプログラムでうまくplt.figureを複数生成する方法が思いつきません．
+```
+def evalOneMax(ind):
+    """Objective function."""
+    evaluation = 0
+    for dish in ind:
+        evaluation += dish.calorie
+    # TODO:グラフ領域を設定
+    #TODO:いい感じにトレーのサイズを外から引数として撮ってくるやり方がわからない直接書いた
+    tray_size = np.array([370, 260])
+    fig = plt.figure()
+    axis = fig.add_subplot(1, 1, 1, xlim=tray_size[0], ylim=tray_size[1], aspect='equal')
+    for dish in ind:
+        axis.add_patch(dish.fig)
+    axis.axis("off")
-'''
+    plt.savefig('tmp.png')
+    fig.delaxes(axis)
+    plt.clf()
+    plt.cla()
+    plt.close()
+    del axis
+    gray_img = cv2.imread('tmp.png')
+    ind.img = gray_img
+    ret, dishes_area_image = cv2.threshold(gray_img, 200, 255, cv2.THRESH_BINARY)#図形がある
+    dishes_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_area_image, cv2.COLOR_RGB2GRAY)))
+    cv2.imwrite('dishes_area.png',dishes_area_image)
+    ret, dishes_overlap_area_image = cv2.threshold(gray_img, 100, 255, cv2.THRESH_BINARY)#重なってて黒い
+    cv2.imwrite('dishes_overlap_area.png',dishes_overlap_area_image)
+    dishes_overlap_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_overlap_area_image, cv2.COLOR_RGB2GRAY)))
+    overlap_per = 1 - dishes_overlap_area/dishes_area
+    return evaluation * overlap_per
+```
+# プログラム全体
+```
 import random
@@ -470,4 +550,6 @@
     main()
-'''
+```