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114 lines
3.3 KiB
114 lines
3.3 KiB
from django.db.models import Q
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from general.views import *
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from menu.models import *
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def search_menu(upperbound, lowerbound, openid):
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menu_search = Dish.objects.filter(Q(time__gt=lowerbound) & Q(time__lte=upperbound))
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return menu_search
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"""
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fridge = FoodStock.objects.filter(owner_id=openid, is_active=1).values()
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quantity = fridge.count()
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for menus in menu_search:
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menu_material_id = Material.objects.filter(Q(dish_id=menus.id) & Q(is_important=1)).values("food_type_id")
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for Id in menu_material_id:
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i = 0
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for food in fridge:
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i += 1
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if food['type_id'] == Id:
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i = 0
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break
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if i == quantity:
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menu_search.remove(menus)
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"""
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"""
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menu_material = Material.objects.get(all)
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fridge = FoodStock.objects.get(all)
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quantity = fridge.count()
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for menus in menu_search:
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id_dish = menus.id
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for menu in menu_material:
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if menu.dish_id == id_dish:
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if menu.is_important == 1:
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id_material = menu.food_type_id
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i = 0
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for food in fridge:
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i += 1
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if food.type_id == id_material:
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i = 0
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break
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if i == quantity:
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menu_search = menu_search.remove(menus)
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"""
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"""
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import numpy as np
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RI_dict = {1: 0, 2: 0, 3: 0.58, 4: 0.90, 5: 1.12, 6: 1.24, 7: 1.32, 8: 1.41, 9: 1.45}
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def get_w(array):
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row = array.shape[0] # 计算出阶数
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a_axis_0_sum = array.sum(axis=0)
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# print(a_axis_0_sum)
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b = array / a_axis_0_sum # 新的矩阵b
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# print(b)
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b_axis_0_sum = b.sum(axis=0)
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b_axis_1_sum = b.sum(axis=1) # 每一行的特征向量
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# print(b_axis_1_sum)
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w = b_axis_1_sum / row # 归一化处理(特征向量)
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nw = w * row
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AW = (w * array).sum(axis=1)
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# print(AW)
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max_max = sum(AW / (row * w))
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# print(max_max)
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CI = (max_max - row) / (row - 1)
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CR = CI / RI_dict[row]
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if CR < 0.1:
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# print(round(CR, 3))
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# print('满足一致性')
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# print(np.max(w))
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# print(sorted(w,reverse=True))
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# print(max_max)
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# print('特征向量:%s' % w)
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return w
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else:
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print(round(CR, 3))
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print('不满足一致性，请进行修改')
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def main(array):
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if type(array) is np.ndarray:
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return get_w(array)
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else:
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print('请输入numpy对象')
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if __name__ == '__main__':
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# 由于地方问题，矩阵我就写成一行了
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e = np.array([[1, 5, 1/2],
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[1/5, 1, 5],
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[2, 1/5, 1]])
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a = np.array([[1, 1 / 3, 1 / 8], [3, 1, 1 / 3], [8, 3, 1]])
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b = np.array([[1, 2, 5], [1 / 2, 1, 2], [1 / 5, 1 / 2, 1]])
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c = np.array([[1, 1, 3], [1, 1, 3], [1 / 3, 1 / 3, 1]])
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d = np.array([[1, 3, 4], [1 / 3, 1, 1], [1 / 4, 1, 1]])
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f = np.array([[1, 4, 1 / 2], [1 / 4, 1, 1 / 4], [2, 4, 1]])
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e = main(e)
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a = main(a)
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b = main(b)
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c = main(c)
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d = main(d)
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f = main(f)
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try:
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res = np.array([a, b, c, d, f])
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ret = (np.transpose(res) * e).sum(axis=1)
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print(ret)
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except TypeError:
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print('数据有误，可能不满足一致性，请进行修改')
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"""
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