Skip to content

A
alpha-mind
Commit 55b727e7 authored by Dr.李's avatar Dr.李
Browse files
Options

more specific naming

parent 03ea0ad0
Hide whitespace changes
Inline Side-by-side
Showing with 400 additions and 54 deletions
alphamind/analysis/factoranalysis.py
View file @ 55b727e7
...@@ -197,7 +197,7 @@ def factor_analysis(factors: pd.DataFrame, ...@@ -197,7 +197,7 @@ def factor_analysis(factors: pd.DataFrame,
risk_target=(risk_lbound, risk_ubound), risk_target=(risk_lbound, risk_ubound),
**kwargs) **kwargs)
else: elif method == 'rank':
# using rank builder # using rank builder
weights = build_portfolio(er, weights = build_portfolio(er,
builder='rank', builder='rank',
......
notebooks/optimizer check.ipynb
View file @ 55b727e7
...@@ -2,15 +2,13 @@ ...@@ -2,15 +2,13 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import sqlalchemy as sahttp://localhost:8888/notebooks/optimizer%20check.ipynb#\n", "import sqlalchemy as sa\n",
"from cvxpy import *\n", "from cvxpy import *\n",
"from cvxopt import *\n", "from cvxopt import *\n",
"from alphamind.data.neutralize import neutralize\n", "from alphamind.data.neutralize import neutralize\n",
...@@ -22,7 +20,7 @@ ...@@ -22,7 +20,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -45,7 +43,7 @@ ...@@ -45,7 +43,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 4,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -57,9 +55,18 @@ ...@@ -57,9 +55,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 248 ms, sys: 40 ms, total: 288 ms\n",
"Wall time: 4.32 s\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"\n", "\n",
...@@ -81,7 +88,7 @@ ...@@ -81,7 +88,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -92,9 +99,18 @@ ...@@ -92,9 +99,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
"Wall time: 3.08 ms\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"factor_values = factor_data[factor_list].values\n", "factor_values = factor_data[factor_list].values\n",
...@@ -104,9 +120,18 @@ ...@@ -104,9 +120,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.29 s, sys: 16 ms, total: 2.3 s\n",
"Wall time: 1.73 s\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"ne_factor_values = neutralize(risk_exposure_values, standardize(winsorize_normal(factor_values)))\n", "ne_factor_values = neutralize(risk_exposure_values, standardize(winsorize_normal(factor_values)))\n",
...@@ -115,18 +140,38 @@ ...@@ -115,18 +140,38 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"(3236, 38)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"risk_exposure_values.shape" "risk_exposure_values.shape"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 192 ms, sys: 28 ms, total: 220 ms\n",
"Wall time: 56.2 ms\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"risk_cov_values = risk_cov_data.loc[risk_factor_columns, risk_factor_columns].astype(float) \n", "risk_cov_values = risk_cov_data.loc[risk_factor_columns, risk_factor_columns].astype(float) \n",
...@@ -135,7 +180,7 @@ ...@@ -135,7 +180,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 11,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -157,9 +202,18 @@ ...@@ -157,9 +202,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 44 ms, sys: 0 ns, total: 44 ms\n",
"Wall time: 17.5 ms\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"w = Variable(n)\n", "w = Variable(n)\n",
...@@ -177,9 +231,52 @@ ...@@ -177,9 +231,52 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"ECOS 2.0.4 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS\n",
"\n",
"It pcost dcost gap pres dres k/t mu step sigma IR | BT\n",
" 0 -2.559e-02 -3.082e+01 +3e+03 9e-01 8e-05 1e+00 7e+00 --- --- 1 1 - | - - \n",
" 1 -2.473e+01 -2.541e+01 +7e+01 1e-01 2e-06 1e-02 2e-01 0.9779 1e-04 1 2 2 | 0 0\n",
" 2 -1.412e+00 -1.437e+00 +3e+00 5e-03 6e-08 5e-04 7e-03 0.9598 2e-03 2 3 3 | 0 0\n",
" 3 -1.019e+00 -1.035e+00 +2e+00 3e-03 4e-08 1e-03 5e-03 0.4692 3e-01 3 4 4 | 0 0\n",
" 4 -9.719e-01 -9.842e-01 +2e+00 3e-03 3e-08 2e-03 4e-03 0.5240 6e-01 3 4 3 | 0 0\n",
" 5 -3.633e-01 -3.662e-01 +4e-01 7e-04 8e-09 6e-04 1e-03 0.8629 1e-01 3 4 4 | 0 0\n",
" 6 -2.058e-01 -2.066e-01 +1e-01 2e-04 2e-09 2e-04 3e-04 0.8197 1e-01 3 4 4 | 0 0\n",
" 7 -1.579e-01 -1.581e-01 +3e-02 5e-05 6e-10 5e-05 7e-05 0.8711 1e-01 3 3 3 | 0 0\n",
" 8 -1.495e-01 -1.496e-01 +1e-02 2e-05 2e-10 2e-05 3e-05 0.7478 2e-01 2 3 3 | 0 0\n",
" 9 -1.468e-01 -1.468e-01 +7e-03 1e-05 2e-10 1e-05 2e-05 0.7025 5e-01 3 4 4 | 0 0\n",
"10 -1.440e-01 -1.440e-01 +1e-03 2e-06 3e-11 3e-06 4e-06 0.9500 1e-01 2 2 2 | 0 0\n",
"11 -1.434e-01 -1.434e-01 +2e-04 3e-07 4e-12 4e-07 5e-07 0.8916 4e-02 3 4 3 | 0 0\n",
"12 -1.433e-01 -1.433e-01 +2e-05 3e-08 3e-13 3e-08 4e-08 0.9253 3e-03 2 2 2 | 0 0\n",
"13 -1.433e-01 -1.433e-01 +7e-07 2e-09 1e-14 1e-09 2e-09 0.9585 5e-04 2 1 2 | 0 0\n",
"14 -1.433e-01 -1.433e-01 +4e-08 1e-09 1e-15 8e-11 1e-10 0.9563 2e-02 3 2 2 | 0 0\n",
"15 -1.433e-01 -1.433e-01 +1e-09 1e-10 8e-17 3e-12 4e-12 0.9676 8e-04 3 2 2 | 0 0\n",
"\n",
"OPTIMAL (within feastol=1.5e-10, reltol=1.0e-08, abstol=1.5e-09).\n",
"Runtime: 0.167465 seconds.\n",
"\n",
"CPU times: user 192 ms, sys: 0 ns, total: 192 ms\n",
"Wall time: 192 ms\n"
]
},
{
"data": {
"text/plain": [
"-0.14330450116208024"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"prob.solve(verbose=True)" "prob.solve(verbose=True)"
...@@ -187,18 +284,63 @@ ...@@ -187,18 +284,63 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"('optimal', -0.14330450116208024)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"prob.status, prob.value" "prob.status, prob.value"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" pcost dcost gap pres dres k/t\n",
" 0: -2.3359e-02 -3.1007e+01 7e+02 1e+01 9e-03 1e+00\n",
" 1: -1.1146e+01 -1.5653e+01 6e+01 1e+00 1e-03 2e-02\n",
" 2: -1.7778e+00 -2.7042e+00 7e+00 3e-01 3e-04 3e-02\n",
" 3: -3.8072e-01 -5.2347e-01 8e-01 5e-02 4e-05 5e-03\n",
" 4: -2.0231e-01 -2.3403e-01 2e-01 1e-02 9e-06 1e-03\n",
" 5: -1.5540e-01 -1.6212e-01 3e-02 2e-03 2e-06 2e-04\n",
" 6: -1.4464e-01 -1.4561e-01 4e-03 3e-04 3e-07 3e-05\n",
" 7: -1.4358e-01 -1.4376e-01 8e-04 6e-05 5e-08 4e-06\n",
" 8: -1.4340e-01 -1.4346e-01 3e-04 2e-05 2e-08 1e-06\n",
" 9: -1.4333e-01 -1.4334e-01 5e-05 4e-06 4e-09 2e-07\n",
"10: -1.4331e-01 -1.4331e-01 8e-06 6e-07 5e-10 3e-08\n",
"11: -1.4330e-01 -1.4330e-01 4e-07 3e-08 3e-11 1e-09\n",
"12: -1.4330e-01 -1.4330e-01 3e-08 2e-09 5e-12 9e-11\n",
"Optimal solution found.\n",
"CPU times: user 248 ms, sys: 12 ms, total: 260 ms\n",
"Wall time: 103 ms\n"
]
},
{
"data": {
"text/plain": [
"-0.14330451119270352"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"prob.solve(verbose=True, solver='CVXOPT')" "prob.solve(verbose=True, solver='CVXOPT')"
...@@ -206,18 +348,51 @@ ...@@ -206,18 +348,51 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"('optimal', -0.14330451119270352)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"prob.status, prob.value" "prob.status, prob.value"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" pcost dcost gap pres dres\n",
" 0: -2.6176e-01 -2.1588e+01 5e+02 2e+01 7e-16\n",
" 1: -2.1701e-01 -1.8470e+01 4e+01 8e-01 1e-15\n",
" 2: -4.4680e-02 -5.2189e+00 6e+00 5e-02 2e-15\n",
" 3: -4.2473e-02 -1.4144e+00 1e+00 1e-02 2e-15\n",
" 4: -9.7185e-02 -3.4512e-01 3e-01 1e-03 1e-15\n",
" 5: -1.2997e-01 -1.8100e-01 5e-02 1e-04 9e-16\n",
" 6: -1.4069e-01 -1.4831e-01 8e-03 1e-05 9e-16\n",
" 7: -1.4300e-01 -1.4373e-01 7e-04 9e-07 8e-16\n",
" 8: -1.4329e-01 -1.4332e-01 3e-05 2e-08 8e-16\n",
" 9: -1.4330e-01 -1.4330e-01 8e-07 3e-10 7e-16\n",
"10: -1.4330e-01 -1.4330e-01 5e-08 6e-12 8e-16\n",
"Optimal solution found.\n",
"CPU times: user 36 ms, sys: 0 ns, total: 36 ms\n",
"Wall time: 10.4 ms\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"P = matrix(sec_cov_values)\n", "P = matrix(sec_cov_values)\n",
...@@ -253,7 +428,7 @@ ...@@ -253,7 +428,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 18,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -264,7 +439,7 @@ ...@@ -264,7 +439,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 19,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -278,7 +453,7 @@ ...@@ -278,7 +453,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 26,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -288,7 +463,7 @@ ...@@ -288,7 +463,7 @@
" w = Variable(n)\n", " w = Variable(n)\n",
"\n", "\n",
" lbound = 0.\n", " lbound = 0.\n",
" ubound = 0.2\n", " ubound = 0.01\n",
"\n", "\n",
" objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\n", " objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
...@@ -302,7 +477,7 @@ ...@@ -302,7 +477,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 27,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
...@@ -316,7 +491,7 @@ ...@@ -316,7 +491,7 @@
" h = np.zeros(2*n)\n", " h = np.zeros(2*n)\n",
" for i in range(n):\n", " for i in range(n):\n",
" G[i, i] = 1.\n", " G[i, i] = 1.\n",
" h[i] = 0.2\n", " h[i] = 0.01\n",
" G[i+n, i] = -1.\n", " G[i+n, i] = -1.\n",
" h[i+n] = 0.\n", " h[i+n] = 0.\n",
"\n", "\n",
...@@ -336,9 +511,32 @@ ...@@ -336,9 +511,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scale(n) cvxpy cvxopt\n",
"200 87.80 7.70\n",
"400 366.22 33.57\n",
"600 838.53 110.26\n",
"800 1725.66 260.92\n",
"1000 3044.70 419.04\n",
"1200 4837.05 700.29\n",
"1400 7262.97 986.25\n",
"1600 10164.79 1630.48\n",
"1800 14156.31 2087.67\n",
"2000 18535.12 2939.18\n",
"2200 23514.86 3707.77\n",
"2400 31820.74 5012.17\n",
"2600 36870.51 6059.89\n",
"2800 44492.36 7884.96\n",
"3000 57279.62 10197.77\n"
]
}
],
"source": [ "source": [
"n_steps = list(range(200, 3001, 200))\n", "n_steps = list(range(200, 3001, 200))\n",
"cvxpy_times = [None] * len(n_steps)\n", "cvxpy_times = [None] * len(n_steps)\n",
...@@ -357,16 +555,150 @@ ...@@ -357,16 +555,150 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 30,
"metadata": { "metadata": {},
"collapsed": true "outputs": [
}, {
"outputs": [], "data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cvxopt</th>\n",
" <th>cvxpy</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Problem Scale (n)</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>7.700</td>\n",
" <td>87.805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>33.566</td>\n",
" <td>366.222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>600</th>\n",
" <td>110.256</td>\n",
" <td>838.531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>260.924</td>\n",
" <td>1725.662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>419.037</td>\n",
" <td>3044.700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1200</th>\n",
" <td>700.287</td>\n",
" <td>4837.045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1400</th>\n",
" <td>986.254</td>\n",
" <td>7262.970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1600</th>\n",
" <td>1630.484</td>\n",
" <td>10164.791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1800</th>\n",
" <td>2087.670</td>\n",
" <td>14156.305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td>2939.184</td>\n",
" <td>18535.122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2200</th>\n",
" <td>3707.774</td>\n",
" <td>23514.858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2400</th>\n",
" <td>5012.174</td>\n",
" <td>31820.735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2600</th>\n",
" <td>6059.892</td>\n",
" <td>36870.513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2800</th>\n",
" <td>7884.956</td>\n",
" <td>44492.361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3000</th>\n",
" <td>10197.773</td>\n",
" <td>57279.625</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cvxopt cvxpy\n",
"Problem Scale (n) \n",
"200 7.700 87.805\n",
"400 33.566 366.222\n",
"600 110.256 838.531\n",
"800 260.924 1725.662\n",
"1000 419.037 3044.700\n",
"1200 700.287 4837.045\n",
"1400 986.254 7262.970\n",
"1600 1630.484 10164.791\n",
"1800 2087.670 14156.305\n",
"2000 2939.184 18535.122\n",
"2200 3707.774 23514.858\n",
"2400 5012.174 31820.735\n",
"2600 6059.892 36870.513\n",
"2800 7884.956 44492.361\n",
"3000 10197.773 57279.625"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"df = pd.DataFrame({'cvxpy': cvxpy_times,\n", "df = pd.DataFrame({'cvxpy': cvxpy_times,\n",
" 'cvxopt': cvxopt_times},\n", " 'cvxopt': cvxopt_times},\n",
" index=n_steps)\n", " index=n_steps)\n",
"df.index.name = 'Problem Scale (n)'" "df.index.name = 'Problem Scale (n)'\n",
"df"
] ]
}, },
{ {
...@@ -383,7 +715,9 @@ ...@@ -383,7 +715,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"df" "df"
...@@ -392,7 +726,9 @@ ...@@ -392,7 +726,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"risk_penlty" "risk_penlty"
...@@ -401,7 +737,9 @@ ...@@ -401,7 +737,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"np.array(sol['x']).flatten()" "np.array(sol['x']).flatten()"
...@@ -410,7 +748,9 @@ ...@@ -410,7 +748,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"np.linalg.det(sec_cov_values_full)" "np.linalg.det(sec_cov_values_full)"
...@@ -419,7 +759,9 @@ ...@@ -419,7 +759,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sec_cov_values_full.diagonal().mean()" "sec_cov_values_full.diagonal().mean()"
...@@ -428,7 +770,9 @@ ...@@ -428,7 +770,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"np.linalg.svd(sec_cov_values_full)" "np.linalg.svd(sec_cov_values_full)"
...@@ -459,7 +803,9 @@ ...@@ -459,7 +803,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sol['dual objective']" "sol['dual objective']"
...@@ -477,7 +823,7 @@ ...@@ -477,7 +823,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python [default]", "display_name": "Python 3",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment