From a6f8087dfbd31a7e7c556e4741e49c78a02468bb Mon Sep 17 00:00:00 2001
From: hh-hh-hh-hh <2025155918@qq.commm>
Date: Fri, 5 Jul 2024 18:57:18 +0800
Subject: [PATCH] =?UTF-8?q?=E9=9F=A9=E6=85=A7=E7=9A=84=E4=BD=9C=E4=B8=9A?=
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---
 7.5/__pycache__/datas.cpython-312.pyc | Bin 0 -> 1027 bytes
 7.5/datas.py                          | 160 ++++++++++++++++++++++++++
 7.5/predict.py                        |  33 ++++++
 3 files changed, 193 insertions(+)
 create mode 100644 7.5/__pycache__/datas.cpython-312.pyc
 create mode 100644 7.5/datas.py
 create mode 100644 7.5/predict.py

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diff --git a/7.5/datas.py b/7.5/datas.py
new file mode 100644
index 0000000..ac6ceff
--- /dev/null
+++ b/7.5/datas.py
@@ -0,0 +1,160 @@
+# 经验是来源于数据
+# 大数据   爬虫
+
+# 模拟太原 和吕梁的房价数据
+# 怎么模拟  城市  面积  户型  是不是学区房  装修的风格
+datas=[
+    {
+        "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
+    {
+        "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":8300
+    },
+    {
+        "city":"吕梁",
+        "area":140,
+        "rooms":3,
+        "school":1,
+        "style":1,
+        "price":8600
+    },
+
+{
+        "city": "吕梁",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 5000
+    },
+    {
+        "city":"吕梁",
+        "area":135,
+        "rooms":3,
+        "school":1,
+        "style":1,
+        "price":5300
+    },
+    {
+        "city":"吕梁",
+        "area":140,
+        "rooms":3,
+        "school":1,
+        "style":1,
+        "price":6300
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8000
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7800
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+
+    },
+    # 模拟
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8300
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8300
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8600
+    },
+
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8500
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8300
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 8600
+    },
+
+
+]
+
diff --git a/7.5/predict.py b/7.5/predict.py
new file mode 100644
index 0000000..3deb612
--- /dev/null
+++ b/7.5/predict.py
@@ -0,0 +1,33 @@
+import numpy as np
+
+# a=np.array([[1,2,3],[1,2,3],[1,2,3]])
+# b=np.array([1,2,3])
+# # 奇异矩阵
+# print(a.dot(b))
+#
+# print(a)
+# print(a.T) # 转置
+# print(np.linalg.pinv(a))  #求逆矩阵
+
+
+from datas import datas
+X=[]
+Y=[]
+cityMark={"吕梁":1,"太原":2}
+for item in datas:
+    single=[]
+    single.append(cityMark[item["city"]])
+    single.append(item["area"])
+    single.append(item["rooms"])
+    single.append(item["school"])
+    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([2,100,2,1,1])))
+# print(theta)
-- 
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