Commit 9602c60f authored by Chanelle Lee's avatar Chanelle Lee
Browse files

Updated test params

parent 3d0ae38c
This source diff could not be displayed because it is too large. You can view the blob instead.
......@@ -97,7 +97,7 @@
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x97bb1d0>]"
"[<matplotlib.lines.Line2D at 0x22e0f4ad240>]"
]
},
"execution_count": 7,
......@@ -129,7 +129,7 @@
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x9851c88>]"
"[<matplotlib.lines.Line2D at 0x22e0f548f28>]"
]
},
"execution_count": 8,
......@@ -157,9 +157,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ss = np.linspace(0.0000001, 10, 1000)\n",
"confusions = [[probConfusion(1, j, s)[0] for s in ss] for j in [2, 3, 4, 5]]\n",
......@@ -169,9 +182,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ss = np.linspace(0.0000001, 10, 1000)\n",
"confusions = [[probConfusion(1, j, s)[0] for s in ss] for j in [2, 3, 4, 5]]\n",
......@@ -181,9 +207,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ss = np.linspace(0.0000001, 5, 1000)\n",
"confusions = [[probConfusion(1, j, s)[0] for s in ss] for j in [2, 3, 4, 5]]\n",
......@@ -193,7 +232,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -202,7 +241,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
......@@ -211,9 +250,96 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sigma</th>\n",
" <th>q2</th>\n",
" <th>q3</th>\n",
" <th>q4</th>\n",
" <th>q5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.000000e-07</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.005105e-03</td>\n",
" <td>4.104751e-122</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.001011e-02</td>\n",
" <td>1.051201e-31</td>\n",
" <td>3.577518e-122</td>\n",
" <td>7.451264e-274</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.501511e-02</td>\n",
" <td>5.734691e-15</td>\n",
" <td>2.733803e-55</td>\n",
" <td>9.343211e-123</td>\n",
" <td>2.336542e-219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.002012e-02</td>\n",
" <td>4.075678e-09</td>\n",
" <td>6.984412e-32</td>\n",
" <td>7.149700e-70</td>\n",
" <td>3.085665e-124</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sigma q2 q3 q4 q5\n",
"0 1.000000e-07 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n",
"1 5.005105e-03 4.104751e-122 0.000000e+00 0.000000e+00 0.000000e+00\n",
"2 1.001011e-02 1.051201e-31 3.577518e-122 7.451264e-274 0.000000e+00\n",
"3 1.501511e-02 5.734691e-15 2.733803e-55 9.343211e-123 2.336542e-219\n",
"4 2.002012e-02 4.075678e-09 6.984412e-32 7.149700e-70 3.085665e-124"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
......@@ -227,7 +353,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
......@@ -243,9 +369,96 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sigma</th>\n",
" <th>q2</th>\n",
" <th>q3</th>\n",
" <th>q4</th>\n",
" <th>q5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>0.370370</td>\n",
" <td>0.375167</td>\n",
" <td>0.262259</td>\n",
" <td>0.169892</td>\n",
" <td>0.101546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>0.375375</td>\n",
" <td>0.376777</td>\n",
" <td>0.265031</td>\n",
" <td>0.173131</td>\n",
" <td>0.104590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>0.380380</td>\n",
" <td>0.378347</td>\n",
" <td>0.267745</td>\n",
" <td>0.176322</td>\n",
" <td>0.107618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0.385385</td>\n",
" <td>0.379878</td>\n",
" <td>0.270401</td>\n",
" <td>0.179466</td>\n",
" <td>0.110627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0.390390</td>\n",
" <td>0.381372</td>\n",
" <td>0.273002</td>\n",
" <td>0.182563</td>\n",
" <td>0.113616</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sigma q2 q3 q4 q5\n",
"74 0.370370 0.375167 0.262259 0.169892 0.101546\n",
"75 0.375375 0.376777 0.265031 0.173131 0.104590\n",
"76 0.380380 0.378347 0.267745 0.176322 0.107618\n",
"77 0.385385 0.379878 0.270401 0.179466 0.110627\n",
"78 0.390390 0.381372 0.273002 0.182563 0.113616"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df.loc[(df['q2'] >= 0.1) & (df['q3'] >= 0.1) & (df['q4'] >= 0.1) & (df['q5'] >= 0.1)].head()"
]
......@@ -271,9 +484,96 @@
},
{
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{
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"text/plain": [
" sigma q2 q3 q4 q5\n",
"56 0.280280 0.337069 0.200187 0.103577 0.046294\n",
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"df.loc[(df['q2'] >= 0.1) & (df['q3'] >= 0.1) & (df['q4'] >= 0.1)].head()"
]
......@@ -299,9 +599,96 @@
},
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" sigma q2 q3 q4 q5\n",
"37 0.185185 0.262259 0.101546 0.028119 0.005455\n",
"38 0.190190 0.267745 0.107618 0.031517 0.006595\n",
"39 0.195195 0.273002 0.113616 0.035049 0.007867\n",
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"41 0.205205 0.282880 0.125357 0.042451 0.010803"
]
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"metadata": {},
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"source": [
"df.loc[(df['q2'] >= 0.1) & (df['q3'] >= 0.1)].head()"
]
......@@ -327,9 +714,96 @@
},
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"execution_count": 19,
"metadata": {},
"outputs": [],
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{
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