diff --git "a/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/.idea/inspectionProfiles/profiles_settings.xml" "b/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/.idea/inspectionProfiles/profiles_settings.xml"
new file mode 100644
index 0000000000000000000000000000000000000000..105ce2da2d6447d11dfe32bfb846c3d5b199fc99
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index 0000000000000000000000000000000000000000..2a01189048dd51552f6ba99bf21b7c67f3613c98
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diff --git "a/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/datas.py" "b/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/datas.py"
new file mode 100644
index 0000000000000000000000000000000000000000..7b52b24aaeffb8ee454870a357acdf3ce7932a57
--- /dev/null
+++ "b/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/datas.py"
@@ -0,0 +1,170 @@
+# 经验就是来源于数据
+# 大数据 爬虫 后天
+
+# 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 1 2 装修的风格 1 2
+#预测系统
+datas=[
+ #吕梁第一类
+ {
+ "city":"吕梁",
+ "area":80,
+ "hx":2,
+ "xqf":1,
+ "style":2,
+ "price":6000
+ },
+ {
+ "city": "吕梁",
+ "area": 80,
+ "hx": 2,
+ "xqf": 2,
+ "style": 2,
+ "price": 6200
+ },
+ {
+ "city": "吕梁",
+ "area": 80,
+ "hx": 3,
+ "xqf": 2,
+ "style": 2,
+ "price": 6500
+ },
+ #吕梁第二类
+ {
+ "city": "吕梁",
+ "area": 100,
+ "hx": 2,
+ "xqf": 2,
+ "style": 1,
+ "price": 7000
+ },
+ {
+ "city": "吕梁",
+ "area": 100,
+ "hx": 2,
+ "xqf": 1,
+ "style": 2,
+ "price": 7600
+ },
+ {
+ "city": "吕梁",
+ "area": 100,
+ "hx": 3,
+ "xqf": 2,
+ "style": 1,
+ "price": 7300
+ },
+ #吕梁第三类
+ {
+ "city": "吕梁",
+ "area": 120,
+ "hx": 2,
+ "xqf": 2,
+ "style": 1,
+ "price": 9000
+ },
+ {
+ "city": "吕梁",
+ "area": 130,
+ "hx": 2,
+ "xqf": 1,
+ "style": 2,
+ "price": 9200
+ },
+ {
+ "city": "吕梁",
+ "area": 150,
+ "hx": 3,
+ "xqf": 1,
+ "style": 2,
+ "price": 10000
+ },
+
+ # 太原第一类
+ {
+ "city": "太原",
+ "area": 120,
+ "hx": 2,
+ "xqf": 2,
+ "style": 1,
+ "price": 10100
+ },
+ {
+ "city": "太原",
+ "area": 130,
+ "hx": 2,
+ "xqf": 1,
+ "style": 2,
+ "price": 10200
+ },
+ {
+ "city": "太原",
+ "area": 150,
+ "hx": 3,
+ "xqf": 1,
+ "style": 2,
+ "price": 10500
+ },
+ # 太原第二类
+ {
+ "city": "太原",
+ "area": 120,
+ "hx": 2,
+ "xqf": 2,
+ "style": 1,
+ "price": 11000
+ },
+ {
+ "city": "太原",
+ "area": 130,
+ "hx": 2,
+ "xqf": 1,
+ "style": 2,
+ "price": 10200
+ },
+ {
+ "city": "太原",
+ "area": 150,
+ "hx": 3,
+ "xqf": 1,
+ "style": 2,
+ "price": 10500
+ },
+ # 太原第三类
+ {
+ "city": "太原",
+ "area": 120,
+ "hx": 2,
+ "xqf": 2,
+ "style": 1,
+ "price": 10400
+ },
+ {
+ "city": "太原",
+ "area": 130,
+ "hx": 2,
+ "xqf": 1,
+ "style": 2,
+ "price": 12000
+ },
+ {
+ "city": "太原",
+ "area": 150,
+ "hx": 3,
+ "xqf": 1,
+ "style": 2,
+ "price": 13500
+ }
+]
+
+# 各 3000 10 1000 1300 1500
+# 城市 面积 房间数量 学区房 装修 各个元素的权重 a1 a2 a3 a4 a5
+# 总价即总分 : p
+# 各个元素*各个元素的权重之和即为分数
+# 城市: x1 例: 县=1 市=2
+# 面积: x2 按平方米计量
+# 房间数量: x3 个数
+# 学区房 : bool 0/1
+# 装修: bool 0/1 是否精装修
\ No newline at end of file
diff --git "a/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/or.py" "b/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/or.py"
new file mode 100644
index 0000000000000000000000000000000000000000..aeee301fbcab7d336d92a69ab1f38213f470fdaa
--- /dev/null
+++ "b/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/or.py"
@@ -0,0 +1,8 @@
+import numpy as np
+
+a=np.array([[1,2,3],[4,5,6],[7,8,9]])
+b=np.array([[1,2,3],[4,5,6],[7,8,9]])
+
+print(np.linalg.pinv(a)) #奇异矩阵 逆
+print(a.T) #转置
+print(a.dot(b)) #点乘
\ No newline at end of file
diff --git "a/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/predict.py" "b/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/predict.py"
new file mode 100644
index 0000000000000000000000000000000000000000..7888aa4b2ef9a38ea297c9bbbde073ca528560a7
--- /dev/null
+++ "b/\350\275\257\345\267\2452\347\217\255 \345\206\257\345\256\207\347\207\225/predict.py"
@@ -0,0 +1,23 @@
+import numpy as np
+
+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["hx"])
+ single.append(item["xqf"])
+ 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(X.T).dot(Y)
+print(theta)
+
+
+