上传文件至 '后端/algorithm'

后端/algorithm/admin.py

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+from django.contrib import admin
+
+# Register your models here.

后端/algorithm/apps.py

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+from django.apps import AppConfig
+
+
+class AlgorithmConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'algorithm'

后端/algorithm/models.py

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+from django.db import models
+
+# Create your models here.

后端/algorithm/tests.py

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+from django.test import TestCase
+
+# Create your tests here.

后端/algorithm/urls.py

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+"""yicanyishi URL Configuration
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/3.2/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+from django.contrib import admin
+from django.urls import path, include
+from . import views
+
+
+urlpatterns = [
+
+]

后端/algorithm/views.py

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