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Date: Fri, 5 Jul 2024 20:46:43 +0800
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---
 7.5/__pycache__/datas.cpython-311.pyc | Bin 0 -> 1150 bytes
 7.5/datas.py                          | 160 ++++++++++++++++++++++++++
 7.5/predict.py                        |  36 ++++++
 3 files changed, 196 insertions(+)
 create mode 100644 7.5/__pycache__/datas.cpython-311.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..b021efa
--- /dev/null
+++ b/7.5/datas.py
@@ -0,0 +1,160 @@
+#经验来源于数据
+# 大数据
+
+#模拟太原和吕梁的房价数据
+
+#怎么模拟   影响因素：城市  面积  户型  学区房 1 是 2 不是 装修 精装 1 毛坯 2 。。。。。。
+
+datas = [
+    #模拟吕梁第一类
+    {
+        "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
+    },
+    #模拟吕梁第二类
+    {
+        "city":"吕梁",
+        "area":135,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":8500
+    },
+    {
+        "city":"吕梁",
+        "area":130,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":8300
+    },
+    {
+        "city":"吕梁",
+        "area":140,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":8600
+    },
+    # 模拟吕梁第三类
+    {
+        "city": "吕梁",
+        "area": 135,
+        "rooms": 2,
+        "school": 2,
+        "style": 2,
+        "price": 6300
+    },
+    {
+        "city": "吕梁",
+        "area": 130,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 6500
+    },
+    {
+        "city": "吕梁",
+        "area": 140,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 5600
+    },
+    # 模拟太原第一类
+    {
+        "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": 10500
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 9800
+    },
+    # 模拟太原第二类
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 10500
+    },
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 10300
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 10600
+    },
+    # 模拟太原第三类
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 2,
+        "school": 2,
+        "style": 2,
+        "price": 8300
+    },
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 8500
+    },
+    {
+        "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..2e1a4e8
--- /dev/null
+++ b/7.5/predict.py
@@ -0,0 +1,36 @@
+#1.先安装一个 科学的计算框架 pip install numpy
+import numpy as np
+from datas import datas
+# a=np.array([[3,2,3],[3,2,3],[3,2,3]])
+# b=np.array([1,2,3])
+# #矩阵的点乘
+# print(a.dot(b))
+# #矩阵的转置
+# print(a.T)
+# #矩阵求逆
+# #奇异矩阵
+# print(np.linalg.pinv(a))
+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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