From ebf386a80e1fd68547d99d5c1b9c5eabd83d0d4b Mon Sep 17 00:00:00 2001
From: qazxsw123456789 <3035820558@qq.com>
Date: Fri, 5 Jul 2024 20:23:57 +0800
Subject: [PATCH] zuoye

---
 __pycache__/datas.cpython-39.pyc | Bin 0 -> 879 bytes
 datas.py                         | 153 +++++++++++++++++++++++++++++++
 predict.py                       |  26 ++++++
 3 files changed, 179 insertions(+)
 create mode 100644 __pycache__/datas.cpython-39.pyc
 create mode 100644 datas.py
 create mode 100644 predict.py

diff --git a/__pycache__/datas.cpython-39.pyc b/__pycache__/datas.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5fad9c134a7a7574e81b9b9454dbaa678d849ba6
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diff --git a/datas.py b/datas.py
new file mode 100644
index 0000000..03dca15
--- /dev/null
+++ b/datas.py
@@ -0,0 +1,153 @@
+datas=[
+
+    # 模拟的吕梁第一类房子
+    {
+        "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,
+    },
+    # 模拟的吕梁第二类房子
+    {
+        "city": "吕梁",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8500,
+    },
+    {
+        "city": "吕梁",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8300,
+    },
+    {
+        "city": "吕梁",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8600,
+    },
+    # 模拟的吕梁第三类房子
+    {
+        "city": "吕梁",
+        "area": 130,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 6500,
+    },
+    {
+        "city": "吕梁",
+        "area": 135,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 6300,
+    },
+    {
+        "city": "吕梁",
+        "area": 140,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 5600,
+    },
+    # 模拟的太原第一类房子
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 10000,
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 9800,
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 9200,
+    },
+    # 模拟的太原第二类房子
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 10500,
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 10300,
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 10600,
+    },
+    # 模拟的太原第三类房子
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 8500,
+    },
+    {
+        "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,
+    },
+]
\ No newline at end of file
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000..bcaa768
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,26 @@
+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"])
+    # 学区房
+    single.append(item["school"])
+    # 装修
+    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.dot(np.array([2,100,2,2])))
+
-- 
Gitee