initial commit

.gitignore

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+.vs/

data/auto-mpg.data

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+mpg	cylinders	displacement	horsepower	weight	acceleration	model year	origin
+18	8	307	130	3504	12	70	1
+15	8	350	165	3693	11.5	70	1
+18	8	318	150	3436	11	70	1
+16	8	304	150	3433	12	70	1
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+15	8	429	198	4341	10	70	1
+14	8	454	220	4354	9	70	1
+14	8	440	215	4312	8.5	70	1
+14	8	455	225	4425	10	70	1
+15	8	390	190	3850	8.5	70	1
+15	8	383	170	3563	10	70	1
+14	8	340	160	3609	8	70	1
+15	8	400	150	3761	9.5	70	1
+14	8	455	225	3086	10	70	1
+24	4	113	95	2372	15	70	3
+22	6	198	95	2833	15.5	70	1
+18	6	199	97	2774	15.5	70	1
+21	6	200	85	2587	16	70	1
+27	4	97	88	2130	14.5	70	3
+26	4	97	46	1835	20.5	70	2
+25	4	110	87	2672	17.5	70	2
+24	4	107	90	2430	14.5	70	2
+25	4	104	95	2375	17.5	70	2
+26	4	121	113	2234	12.5	70	2
+21	6	199	90	2648	15	70	1
+10	8	360	215	4615	14	70	1
+10	8	307	200	4376	15	70	1
+11	8	318	210	4382	13.5	70	1
+9	8	304	193	4732	18.5	70	1
+27	4	97	88	2130	14.5	71	3
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+25	4	113	95	2228	14	71	3
+25	4	98	?	2046	19	71	1
+19	6	232	100	2634	13	71	1
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+17	6	250	100	3329	15.5	71	1
+19	6	250	88	3302	15.5	71	1
+18	6	232	100	3288	15.5	71	1
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+14	8	351	153	4154	13.5	71	1
+14	8	318	150	4096	13	71	1
+12	8	383	180	4955	11.5	71	1
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+18	6	258	110	2962	13.5	71	1
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+31	4	71	65	1773	19	71	3
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+24	4	113	95	2278	15.5	72	3
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+11	8	429	208	4633	11	72	1
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+13	8	400	190	4422	12.5	72	1
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+31	4	79	67	2000	16	74	2
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graph.py

@@ -0,0 +1,22 @@
+import matplotlib.pyplot as plt
+
+class Graph:
+	def __init__(self, **kwargs):
+		self.f = plt.figure()
+		self.ax = self.f.add_subplot(111)
+		self.line = None
+
+	def draw_variable(self, x, y):
+		self.x = x
+		self.y = y
+		self.ax.scatter(x, y)
+		plt.ion()
+		plt.show()
+
+	def draw_line(self, x, y):
+		if self.line is not None:
+			self.line.remove()
+		self.line = plt.Line2D(x, y, color='r')
+		self.ax.add_line(self.line)
+		plt.draw()
+		plt.pause(0.000000001)

regression2.py

@@ -0,0 +1,44 @@
+import warnings
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+
+import numpy as np
+import pandas as pd
+
+
+def load_data():
+	df = pd.read_csv('data/sample.txt', sep=',', header=None)
+
+	df[2] = pd.Series([1]*len(df[0]))
+	df = df.reindex(columns=[1, 2, 0])
+
+	return df
+
+def get_cost():
+	
+	pass
+
+def get_gradient():
+	pass
+
+
+
+df = load_data()
+
+
+y = df.values[:, 0]
+x = df.values[:, 1:]
+m, n = x.shape
+w = np.zeros(n)
+a = 0.001
+
+
+sum_ = 0
+gap = 0.001
+
+while True:
+	h = x@w
+	cost = ((h-y)**2).mean()/2
+	gradient = a*(h-y)@x
+
+
+pass

regressions.py

@@ -0,0 +1,112 @@
+import numpy as np
+import graph
+import pandas as pd
+#import tensorflow as tf
+
+
+def normalize_feature(x: np.array) -> (np.array, float, float):
+	mu = x.mean(0)
+	std = x.std(0)
+	std[std == 0] = 1
+	x_norm = (x-mu)/std
+	return x_norm, mu, std
+
+def add_column(x: np.array):
+	n, m = x.shape
+	return np.c_[np.ones((n, 1)), x.reshape((n, m))]
+
+def split_data(x, y):
+	m, n = x.shape
+	test_size = int(m*0.2)
+	test_indices = np.random.choice(m, test_size)
+
+	train_x = np.delete(x, test_indices, axis=0)
+	test_x = x[test_indices]
+	train_y = np.delete(y, test_indices, axis=0)
+	test_y = y[test_indices]
+
+	return train_x, test_x, train_y, test_y
+
+def get_regularization_term(w: np.array, wlambda: float) -> np.array:
+	m = len(w)
+	return wlambda*(w**2).sum()/(2*m)
+
+def get_derived_regularization_term(w: np.array, wlambda: float, alpha: float) -> np.array:
+	m = len(w)
+	return np.r_[0, alpha*wlambda*w[1:]/m]
+
+
+#data = np.array([
+#	[1,3+1],
+#	[2,6+1],
+#	[3,9+1],
+#	[4,12+1],
+#	[5,15+1],
+#	])
+
+#data = np.loadtxt('data/ex1data1.txt', delimiter=',')
+# train excercize
+#data = np.loadtxt('data/train.csv', delimiter=',', skiprows=1, comments='#')
+# auto mpg
+#df = pd.read_csv('data/auto-mpg.data', sep='\t')
+#df = df.drop(df.loc[df['horsepower'] == '?'].index)
+#df = df.convert_objects(convert_numeric=True)
+#data = df.values
+# Airfoil Self-Noise Data Set
+#df = pd.read_csv('data/airfoil_self_noise.dat', sep='\t')
+#df = df.convert_objects(convert_numeric=True)
+#data = df.values
+
+df = pd.read_csv('data/mlr01.csv', sep=',')
+data = df.values
+
+
+
+x_init = data[:, 0:-1]
+y_init = data[:, -1]
+
+x, mu, std = normalize_feature(x_init)
+x = add_column(x)
+x, test_x, y, test_y = split_data(x, y_init)
+n, m = x.shape
+
+w = np.zeros(m)
+
+is_2d = (m == 2)
+
+if is_2d == True:
+	g = graph.Graph()
+	g.draw_variable(x_init, y)
+
+alpha = 0.001
+
+iter = 0
+sum_gradient = 0
+display_gap = 0.5
+wlambda = 0.001
+
+while True:
+	h = x@w
+	cost = ((h - y)**2).mean() + get_regularization_term(w, wlambda)
+	gradient = alpha*((h - y)*x.T).mean(1) + get_derived_regularization_term(w, wlambda, alpha)
+	w -= gradient
+
+	sum_gradient += abs(gradient.sum())
+	if sum_gradient >= display_gap:
+		sum_gradient -= display_gap
+		print("{} : {}".format(iter, cost))
+		
+		if is_2d == True:
+			g.draw_line(x_init, h)
+
+	iter += 1
+
+	if max(abs(gradient)) < 1e-5:
+		if is_2d:
+			g.draw_line(x_init, h)
+		break
+print('iteration: {}'.format(iter))
+
+for i in range(len(test_x)):
+	h = test_x[i]@w
+	print('h: {:.2f} / y: {}'.format(h, test_y[i]))

regressions.pyproj

@@ -0,0 +1,50 @@
+<?xml version="1.0" encoding="utf-8"?>
+<Project DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003" ToolsVersion="4.0">
+  <PropertyGroup>
+    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
+    <SchemaVersion>2.0</SchemaVersion>
+    <ProjectGuid>db253b3a-f559-48b8-9804-846029a6ebef</ProjectGuid>
+    <ProjectHome>.</ProjectHome>
+    <StartupFile>regression2.py</StartupFile>
+    <SearchPath>
+    </SearchPath>
+    <WorkingDirectory>.</WorkingDirectory>
+    <OutputPath>.</OutputPath>
+    <Name>regressions</Name>
+    <RootNamespace>regressions</RootNamespace>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Debug' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Release' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <ItemGroup>
+    <Compile Include="graph.py">
+      <SubType>Code</SubType>
+    </Compile>
+    <Compile Include="regression2.py">
+      <SubType>Code</SubType>
+    </Compile>
+    <Compile Include="regressions.py" />
+    <Compile Include="test.py">
+      <SubType>Code</SubType>
+    </Compile>
+  </ItemGroup>
+  <PropertyGroup>
+    <VisualStudioVersion Condition="'$(VisualStudioVersion)' == ''">10.0</VisualStudioVersion>
+    <PtvsTargetsFile>$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets</PtvsTargetsFile>
+  </PropertyGroup>
+  <Import Condition="Exists($(PtvsTargetsFile))" Project="$(PtvsTargetsFile)" />
+  <Import Condition="!Exists($(PtvsTargetsFile))" Project="$(MSBuildToolsPath)\Microsoft.Common.targets" />
+  <!-- Uncomment the CoreCompile target to enable the Build command in
+       Visual Studio and specify your pre- and post-build commands in
+       the BeforeBuild and AfterBuild targets below. -->
+  <!--<Target Name="CoreCompile" />-->
+  <Target Name="BeforeBuild">
+  </Target>
+  <Target Name="AfterBuild">
+  </Target>
+</Project>

regressions.sln

@@ -0,0 +1,20 @@
+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio 14
+VisualStudioVersion = 14.0.25420.1
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "regressions", "regressions.pyproj", "{DB253B3A-F559-48B8-9804-846029A6EBEF}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Any CPU = Debug|Any CPU
+		Release|Any CPU = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{DB253B3A-F559-48B8-9804-846029A6EBEF}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{DB253B3A-F559-48B8-9804-846029A6EBEF}.Release|Any CPU.ActiveCfg = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+EndGlobal

test.py

@@ -0,0 +1,37 @@
+import numpy as np
+
+x = np.ones((2, 3, 4,5 ))
+print('x:')
+print(x.shape)
+print(x)
+
+y = np.transpose(x)
+print('y:')
+print(y.shape)
+print(y)
+
+x = np.array([1,2,3,4,5])
+
+print(x)
+print(x.mean())
+print(x.std())
+print(10**(1/2))
+
+
+data = np.array([
+	[1,3+1],
+	[2,6+1],
+	[3,9+1],
+	[4,12+1],
+	[5,15+1],
+	])
+
+n = data.shape[0]
+x_init = data[:,0]
+x = np.c_[x_init.reshape(n, -1), np.ones((n, 1))]
+print(x)
+
+x = np.array([1,2,3,4,5,6,7,8,9,10])
+print(x**2)
+x = x**2
+print(x.sum())