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 <14647838+chengyixin111@user.noreply.gitee.com>
Date: Fri, 5 Jul 2024 21:39:40 +0800
Subject: [PATCH] =?UTF-8?q?=E6=88=90=E6=80=A1=E6=98=95=E7=9A=84=E4=BD=9C?=
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---
 07.05/0705/__pycache__/datas.cpython-312.pyc | Bin 0 -> 1073 bytes
 07.05/0705/datas.py                          | 172 +++++++++++++++++++
 07.05/0705/predict.py                        |  26 +++
 3 files changed, 198 insertions(+)
 create mode 100644 07.05/0705/__pycache__/datas.cpython-312.pyc
 create mode 100644 07.05/0705/datas.py
 create mode 100644 07.05/0705/predict.py

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diff --git a/07.05/0705/datas.py b/07.05/0705/datas.py
new file mode 100644
index 0000000..57e5d36
--- /dev/null
+++ b/07.05/0705/datas.py
@@ -0,0 +1,172 @@
+# 经验来源于数据
+# 大数据 很多的数据
+
+# 预测地方房价
+# 预测太原，吕梁房价
+# 1. 模拟太原和吕梁的房价数据
+# 模拟吕梁的房子数据
+datas=[
+    # 模拟吕梁的第一类房子
+    {
+        "city":"吕梁",
+        "area":100,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":8000
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7800
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    # 模拟吕梁的第二类房子
+    {
+        "city":"吕梁",
+        "area":130,
+        "rooms":3,
+        "school":1,
+        "style":1,
+        "price":8300
+    },
+    {
+        "city": "吕梁",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8450
+    },
+    {
+        "city": "吕梁",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8600
+    },
+    # 模拟吕梁的第三类房子
+    {
+        "city":"吕梁",
+        "area":130,
+        "rooms":3,
+        "school":2,
+        "style":2,
+        "price":7000
+    },
+    {
+        "city": "吕梁",
+        "area": 135,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 6500
+    },
+    {
+        "city": "吕梁",
+        "area": 140,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 6200
+    },
+]
+# 模拟太原房价数据
+datas=[
+    # 模拟太原的第一类房子
+    {
+        "city":"太原",
+        "area":100,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":10100
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 10300
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 10500
+    },
+    # 模拟太原的第二类房子
+    {
+        "city":"太原",
+        "area":135,
+        "rooms":3,
+        "school":1,
+        "style":1,
+        "price":12120
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 12300
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 12600
+    },
+    # 模拟太原的第三类房子
+    {
+        "city":"太原",
+        "area":130,
+        "rooms":3,
+        "school":2,
+        "style":2,
+        "price":8000
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 8300
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 8600
+    },
+    # 模拟房价数据的权重计算
+    # 3000 10  1000 1300 1500
+    # 太原2 100 3    1    1
+    # 3000+1400+3000+2600+3000
+    # 城市 x1 面积 x2 房间数 x3 学校 x4 样式 x5
+    # x1=1 x2=100 x3=3 x4=1 x5=1
+    # a1*1+a2*2+a3*3+a4*4+a5*5=y
+    # [a1,a2,......] 数学模型 机器学习 机器学习软件开发
+
+    ]
+
diff --git a/07.05/0705/predict.py b/07.05/0705/predict.py
new file mode 100644
index 0000000..b21590e
--- /dev/null
+++ b/07.05/0705/predict.py
@@ -0,0 +1,26 @@
+# 1. 安装一个科学与计算的框架
+import numpy as np
+from datas import datas
+X=[]
+Y=[]
+cityMark={"吕梁":1,"太原":2}
+for item in datas:
+    single=[]
+    #城市
+    single.append(cityMark[item["city"]])
+    #面积
+    single.append(item["area"])
+    #rooms
+    single.append(item["rooms"])
+    #school
+    single.append(item["school"])
+    #style
+    single.append(item["style"])
+    X.append(single)
+    Y.append(item["price"])
+
+X = np.array(X)
+Y = np.array(Y)
+theta = np.linalg.pinv(X.T.dot(X)).dot(X.T).dot(Y)
+print(theta)
+
-- 
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