diff --git a/code/ex1-linear regression/1.linear_regreesion_v1.ipynb b/code/ex1-linear regression/1.linear_regreesion_v1.ipynb
index a793d0e7..68d9651f 100644
--- a/code/ex1-linear regression/1.linear_regreesion_v1.ipynb
+++ b/code/ex1-linear regression/1.linear_regreesion_v1.ipynb
@@ -11,10 +11,8 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
@@ -27,10 +25,8 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('ex1data1.txt', names=['population', 'profit'])#读取数据并赋予列名"
@@ -38,25 +34,25 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
- "\n",
"
\n",
" \n",
@@ -105,7 +101,7 @@
"4 5.8598 6.8233"
]
},
- "execution_count": 3,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -116,7 +112,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -147,14 +143,14 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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3vBPlfgDg8jmmh9Qa4ZbxodwPAFw+RwXSjqIvWrQybLjnnbgeCQAun2OG7HwX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\ngg==\n",
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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -162,7 +158,7 @@
}
],
"source": [
- "sns.lmplot('population', 'profit', df, size=6, fit_reg=False)\n",
+ "sns.lmplot('population', 'profit', df, height=6, fit_reg=False)\n",
"plt.show()"
]
},
diff --git a/markdown/week2.md b/markdown/week2.md
index 15d4e301..15699b8f 100644
--- a/markdown/week2.md
+++ b/markdown/week2.md
@@ -244,7 +244,7 @@ $J(\theta )=\frac{1}{2}{{\left( X\theta -y\right)}^{T}}\left( X\theta -y \right)
$=\frac{1}{2}\left( {{\theta }^{T}}{{X}^{T}}-{{y}^{T}} \right)\left(X\theta -y \right)$
- $=\frac{1}{2}\left( {{\theta }^{T}}{{X}^{T}}X\theta -{{\theta}^{T}}{{X}^{T}}y-{{y}^{T}}X\theta -{{y}^{T}}y \right)$
+ $=\frac{1}{2}\left( {{\theta }^{T}}{{X}^{T}}X\theta -{{\theta}^{T}}{{X}^{T}}y-{{y}^{T}}X\theta +{{y}^{T}}y \right)$
接下来对$J(\theta )$偏导,需要用到以下几个矩阵的求导法则:
@@ -254,9 +254,9 @@ $\frac{d{{X}^{T}}AX}{dX}=2AX$
所以有:
-$\frac{\partial J\left( \theta \right)}{\partial \theta }=\frac{1}{2}\left(2{{X}^{T}}X\theta -{{X}^{T}}y -{}({{y}^{T}}X )^{T}-0 \right)$
+$\frac{\partial J\left( \theta \right)}{\partial \theta }=\frac{1}{2}\left(2{{X}^{T}}X\theta -{{X}^{T}}y -{}({{y}^{T}}X )^{T}+0 \right)$
-$=\frac{1}{2}\left(2{{X}^{T}}X\theta -{{X}^{T}}y -{{X}^{T}}y -0 \right)$
+$=\frac{1}{2}\left(2{{X}^{T}}X\theta -{{X}^{T}}y -{{X}^{T}}y +0 \right)$
$={{X}^{T}}X\theta -{{X}^{T}}y$