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Merge pull request #17 from titanscout2022/equation.py-testing

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merge equation.py-testing with master
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Arthur Lu 2020-05-15 16:01:41 -05:00 committed by GitHub
commit b376f7c0c5
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 217 additions and 9 deletions
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1
.devcontainer/devcontainer.json
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@ -21,6 +21,7 @@
},
"extensions": [
"mhutchie.git-graph",
"donjayamanne.jupyter",
],
"postCreateCommand": "pip install -r analysis-master/analysis-amd64/requirements.txt"
}

35
analysis-master/analysis/.ipynb_checkpoints/equation-checkpoint.ipynb Normal file
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@ -0,0 +1,35 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"string = \"3+4+5\"\n",
"re.sub(\"\\d+[+]{1}\\d+\", string, sum([int(i) for i in re.split(\"[+]{1}\", re.search(\"\\d+[+]{1}\\d+\", string).group())]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

26
analysis-master/analysis/analysis.py
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@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.2.1.002"
__version__ = "1.2.2.000"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.2.2.000:
- changed output of regressions to function strings instead of list of coefficients
1.2.1.002:
- renamed ArrayTest class to Array
1.2.1.001:
@ -412,7 +414,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(lin, X, y)
regressions.append((popt.flatten().tolist(), None))
coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*x+" + str(coeffs[1]))
except Exception as e:
@ -428,7 +431,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(log, X, y)
regressions.append((popt.flatten().tolist(), None))
coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
except Exception as e:
@ -444,7 +448,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(exp, X, y)
regressions.append((popt.flatten().tolist(), None))
coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
except Exception as e:
@ -466,10 +471,14 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
params = model.steps[1][1].intercept_.tolist()
params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::])
params.flatten()
params = params.tolist()
params = params.flatten().tolist()
plys.append(params)
temp = ""
counter = 0
for param in params:
temp += "(" + str(param) + "*x^" + str(counter) + ")"
counter += 1
plys.append(temp)
regressions.append(plys)
@ -483,7 +492,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
popt, pcov = scipy.optimize.curve_fit(sig, X, y)
regressions.append((popt.flatten().tolist(), None))
coeffs = popt.flatten().tolist()
regressions.append(str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
except Exception as e:

162
analysis-master/analysis/equation.ipynb Normal file
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@ -0,0 +1,162 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from decimal import Decimal\n",
"from functools import reduce"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def add(string):\n",
" while(len(re.findall(\"[+]{1}[-]?\", string)) != 0):\n",
" string = re.sub(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x + y), [Decimal(i) for i in re.split(\"[+]{1}\", re.search(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
" return string"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def sub(string):\n",
" while(len(re.findall(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string)) != 0):\n",
" g = re.search(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string).group()\n",
" if(re.search(\"[-]{1,2}\", g).group() == \"-\"):\n",
" r = re.sub(\"[-]{1}\", \"+-\", g, 1)\n",
" string = re.sub(g, r, string, 1)\n",
" elif(re.search(\"[-]{1,2}\", g).group() == \"--\"):\n",
" r = re.sub(\"[-]{2}\", \"+\", g, 1)\n",
" string = re.sub(g, r, string, 1)\n",
" else:\n",
" pass\n",
" return string"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def mul(string):\n",
" while(len(re.findall(\"[*]{1}[-]?\", string)) != 0):\n",
" string = re.sub(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x * y), [Decimal(i) for i in re.split(\"[*]{1}\", re.search(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
" return string"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def div(string):\n",
" while(len(re.findall(\"[/]{1}[-]?\", string)) != 0):\n",
" string = re.sub(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x / y), [Decimal(i) for i in re.split(\"[/]{1}\", re.search(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
" return string"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def exp(string):\n",
" while(len(re.findall(\"[\\^]{1}[-]?\", string)) != 0):\n",
" string = re.sub(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x ** y), [Decimal(i) for i in re.split(\"[\\^]{1}\", re.search(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
" return string"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def evaluate(string):\n",
" string = exp(string)\n",
" string = div(string)\n",
" string = mul(string)\n",
" string = sub(string)\n",
" print(string)\n",
" string = add(string)\n",
" return string"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "error",
"ename": "SyntaxError",
"evalue": "unexpected EOF while parsing (<ipython-input-13-f9fb4aededd9>, line 1)",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-13-f9fb4aededd9>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m def parentheses(string):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n"
]
}
],
"source": [
"def parentheses(string):"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "-158456325028528675187087900672.000000+0.8\n"
},
{
"output_type": "execute_result",
"data": {
"text/plain": "'-158456325028528675187087900672.000000'"
},
"metadata": {},
"execution_count": 22
}
],
"source": [
"string = \"8^32*4/-2+0.8\"\n",
"evaluate(string)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6-final"
}
},
"nbformat": 4,
"nbformat_minor": 4
}