While practicing the following dynamic programming question on HackerRank, I got the 'timeout' error. The code run successfully on some test examples, but got 'timeout' errors on others. I'm wondering how can I further improve the code.
The question is
Given an amount and the denominations of coins available, determine how many ways change can be made for amount. There is a limitless supply of each coin type.
Example:
n = 3
c = [8, 3, 1, 2]
There are 3 ways to make change for n=3 : {1, 1, 1}, {1, 2}, and {3}.
My current code is
import math
import os
import random
import re
import sys
from functools import lru_cache
#
# Complete the 'getWays' function below.
#
# The function is expected to return a LONG_INTEGER.
# The function accepts following parameters:
# 1. INTEGER n
# 2. LONG_INTEGER_ARRAY c
#
def getWays(n, c):
# Write your code here
#c = sorted(c)
#lru_cache
def get_ways_recursive(n, cur_idx):
cur_denom = c[cur_idx]
n_ways = 0
if n == 0:
return 1
if cur_idx == 0:
return 1 if n % cur_denom == 0 else 0
for k in range(n // cur_denom + 1):
n_ways += get_ways_recursive(n - k * cur_denom,
cur_idx - 1)
return n_ways
return get_ways_recursive(n, len(c) - 1)
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
first_multiple_input = input().rstrip().split()
n = int(first_multiple_input[0])
m = int(first_multiple_input[1])
c = list(map(int, input().rstrip().split()))
# Print the number of ways of making change for 'n' units using coins having the values given by 'c'
ways = getWays(n, c)
fptr.write(str(ways) + '\n')
fptr.close()
It timed out on the following test example
166 23 # 23 is the number of coins below.
5 37 8 39 33 17 22 32 13 7 10 35 40 2 43 49 46 19 41 1 12 11 28
My question is why Python doesn't execute the print statement in the Exception Handling code below. I am trying to calculate the log of volumes for a bunch of stocks. Each stock has 1259 volume values. But Python generates a RunTimeWarning "divide by zero encountered in log". So I try to use Exception Handling to locate where the log input is zero, but Python doesn't execute the print statement under except. The print statement is supposed to print the name of the stock and the index in the array where the volume is zero. Why?
Here is the code:
for i, stock in enumerate(df.columns):
volumes = df[stock].to_numpy()
for r in range(len(volumes)): # len(volumes) = 1259
try:
v = np.log(volumes[r])
except:
print(stock, r)
Here is the Error that follows after the RunTimeWarning.
LinAlgError Traceback (most recent call last)
<ipython-input-6-6aa283671e2c> in <module>
13 closes = df_close[stock].to_numpy()
14 volumes = df_vol[stock].to_numpy()
---> 15 indicator_values_all_stocks[i] = indicator.price_volume_fit(volumes, closes, histLength)
16
17 indicator_values_all_stocks_no_NaN = indicator_values_all_stocks[:, ~np.isnan(indicator_values_all_stocks).any(axis=0)]
~\Desktop\Python Projects Organized\Finance\Indicator Statistics\B.57. Price Volume Fit\indicator.py in price_volume_fit(volumes, closes, histLength)
1259 x = log_volumes[i - histLength:i]
1260 y = log_prices[i - histLength:i]
-> 1261 model = np.polyfit(x, y, 1, full = True)
1262 slope[i] = model[0][0]
1263
<__array_function__ internals> in polyfit(*args, **kwargs)
c:\users\donald seger\miniconda3\envs\tensorflow\lib\site-packages\numpy\lib\polynomial.py in polyfit(x, y, deg, rcond, full, w, cov)
629 scale = NX.sqrt((lhs*lhs).sum(axis=0))
630 lhs /= scale
--> 631 c, resids, rank, s = lstsq(lhs, rhs, rcond)
632 c = (c.T/scale).T # broadcast scale coefficients
633
<__array_function__ internals> in lstsq(*args, **kwargs)
c:\users\donald seger\miniconda3\envs\tensorflow\lib\site-packages\numpy\linalg\linalg.py in lstsq(a, b, rcond)
2257 # lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis
2258 b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
-> 2259 x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
2260 if m == 0:
2261 x[...] = 0
c:\users\donald seger\miniconda3\envs\tensorflow\lib\site-packages\numpy\linalg\linalg.py in _raise_linalgerror_lstsq(err, flag)
107
108 def _raise_linalgerror_lstsq(err, flag):
--> 109 raise LinAlgError("SVD did not converge in Linear Least Squares")
110
111 def get_linalg_error_extobj(callback):
LinAlgError: SVD did not converge in Linear Least Squares
I want to solve a continuum mechanics problem thanks to FEniCS. I apply pressure and take into account the weight. But when I add the thermoelasticity component, it doesn't work anymore.
Here is my code :
from dolfin import *
from fenics import *
from ufl import nabla_div
from ufl import as_tensor
import matplotlib.pyplot as plt
import numpy as np
E = Constant(100*10**9)
nu = Constant(0.3)
Lg = 0.01; W = 0.2
mu = E/(2+2*nu)
rho = Constant(2200)
delta = W/Lg
gamma = 0.4*delta**2
beta = 8
lambda_ = (E*nu)/((1+nu)*(1-2*nu))
alpha = 1.2*(10**(-8))
deltaT = Constant(50)
Kt = E*alph*deltaT/(1-2*nu)
g = 9.81
tol = 1E-14
# Create mesh and define function space
mesh = RectangleMesh(Point(-2., 0.),Point(2., 10.), 80, 200)
V = VectorFunctionSpace(mesh, "P", 1)
# Define boundary condition
def clamped_boundary(x, on_boundary):
return on_boundary and x[1] < tol
class UpFace(SubDomain):
def inside(self, x, on_boundary):
return on_boundary and (x[1] > 10 - tol)
ueN = UpFace()
boundaries = MeshFunction("size_t", mesh, mesh.topology().dim()-1, 0)
ueN.mark(boundaries, 1)
ds = Measure("ds", domain=mesh, subdomain_data=boundaries)
bc = DirichletBC(V, Constant((0, 0)), clamped_boundary)
def epsilon(u):
return 0.5*(nabla_grad(u) + nabla_grad(u).T)
def sigma(u):
return (lambda_*nabla_div(u) - Kt)*Identity(d) + (2*mu)*epsilon(u)
# Define variational problem
u = TrialFunction(V)
d = u.geometric_dimension() # space dimension
v = TestFunction(V)
f = Constant((0,-rho*g))
T = Constant((0, 0))
Pr = Constant((0, -2*10**9))
a = inner(sigma(u), epsilon(v))*dx
L = dot(f, v)*dx + dot(T, v)*ds + dot(Pr,v)*ds(1)
# Compute solution
u = Function(V)
solve(a == L, u, bc)
# Plot solution
plot(u, mode="displacement", color= "red")
plt.colorbar(plot(u))
I get this error message :
---------------------------------------------------------------------------
ArityMismatch Traceback (most recent call last)
<ipython-input-54-805d7c5b704f> in <module>
17 # Compute solution
18 u = Function(V)
---> 19 solve(a == L, u, bc)
20
21 # Plot solution
/usr/lib/python3/dist-packages/dolfin/fem/solving.py in solve(*args, **kwargs)
218 # tolerance)
219 elif isinstance(args[0], ufl.classes.Equation):
--> 220 _solve_varproblem(*args, **kwargs)
221
222 # Default case, just call the wrapped C++ solve function
/usr/lib/python3/dist-packages/dolfin/fem/solving.py in _solve_varproblem(*args, **kwargs)
240 # Create problem
241 problem = LinearVariationalProblem(eq.lhs, eq.rhs, u, bcs,
--> 242 form_compiler_parameters=form_compiler_parameters)
243
244 # Create solver and call solve
/usr/lib/python3/dist-packages/dolfin/fem/problem.py in __init__(self, a, L, u, bcs, form_compiler_parameters)
54 else:
55 L = Form(L, form_compiler_parameters=form_compiler_parameters)
---> 56 a = Form(a, form_compiler_parameters=form_compiler_parameters)
57
58 # Initialize C++ base class
/usr/lib/python3/dist-packages/dolfin/fem/form.py in __init__(self, form, **kwargs)
42
43 ufc_form = ffc_jit(form, form_compiler_parameters=form_compiler_parameters,
---> 44 mpi_comm=mesh.mpi_comm())
45 ufc_form = cpp.fem.make_ufc_form(ufc_form[0])
46
/usr/lib/python3/dist-packages/dolfin/jit/jit.py in mpi_jit(*args, **kwargs)
45 # Just call JIT compiler when running in serial
46 if MPI.size(mpi_comm) == 1:
---> 47 return local_jit(*args, **kwargs)
48
49 # Default status (0 == ok, 1 == fail)
/usr/lib/python3/dist-packages/dolfin/jit/jit.py in ffc_jit(ufl_form, form_compiler_parameters)
95 p.update(dict(parameters["form_compiler"]))
96 p.update(form_compiler_parameters or {})
---> 97 return ffc.jit(ufl_form, parameters=p)
98
99
/usr/lib/python3/dist-packages/ffc/jitcompiler.py in jit(ufl_object, parameters, indirect)
215
216 # Inspect cache and generate+build if necessary
--> 217 module = jit_build(ufl_object, module_name, parameters)
218
219 # Raise exception on failure to build or import module
/usr/lib/python3/dist-packages/ffc/jitcompiler.py in jit_build(ufl_object, module_name, parameters)
131 name=module_name,
132 params=params,
--> 133 generate=jit_generate)
134 return module
135
/usr/lib/python3/dist-packages/dijitso/jit.py in jit(jitable, name, params, generate, send, receive, wait)
163 elif generate:
164 # 1) Generate source code
--> 165 header, source, dependencies = generate(jitable, name, signature, params["generator"])
166 # Ensure we got unicode from generate
167 header = as_unicode(header)
/usr/lib/python3/dist-packages/ffc/jitcompiler.py in jit_generate(ufl_object, module_name, signature, parameters)
64
65 code_h, code_c, dependent_ufl_objects = compile_object(ufl_object,
---> 66 prefix=module_name, parameters=parameters, jit=True)
67
68 # Jit compile dependent objects separately,
/usr/lib/python3/dist-packages/ffc/compiler.py in compile_form(forms, object_names, prefix, parameters, jit)
141 """This function generates UFC code for a given UFL form or list of UFL forms."""
142 return compile_ufl_objects(forms, "form", object_names,
--> 143 prefix, parameters, jit)
144
145
/usr/lib/python3/dist-packages/ffc/compiler.py in compile_ufl_objects(ufl_objects, kind, object_names, prefix, parameters, jit)
183 # Stage 1: analysis
184 cpu_time = time()
--> 185 analysis = analyze_ufl_objects(ufl_objects, kind, parameters)
186 _print_timing(1, time() - cpu_time)
187
/usr/lib/python3/dist-packages/ffc/analysis.py in analyze_ufl_objects(ufl_objects, kind, parameters)
88 # Analyze forms
89 form_datas = tuple(_analyze_form(form, parameters)
---> 90 for form in forms)
91
92 # Extract unique elements accross all forms
/usr/lib/python3/dist-packages/ffc/analysis.py in <genexpr>(.0)
88 # Analyze forms
89 form_datas = tuple(_analyze_form(form, parameters)
---> 90 for form in forms)
91
92 # Extract unique elements accross all forms
/usr/lib/python3/dist-packages/ffc/analysis.py in _analyze_form(form, parameters)
172 do_apply_geometry_lowering=True,
173 preserve_geometry_types=(Jacobian,),
--> 174 do_apply_restrictions=True)
175 elif r == "tsfc":
176 try:
/usr/lib/python3/dist-packages/ufl/algorithms/compute_form_data.py in compute_form_data(form, do_apply_function_pullbacks, do_apply_integral_scaling, do_apply_geometry_lowering, preserve_geometry_types, do_apply_default_restrictions, do_apply_restrictions, do_estimate_degrees, do_append_everywhere_integrals, complex_mode)
416 preprocessed_form = remove_complex_nodes(preprocessed_form)
417
--> 418 check_form_arity(preprocessed_form, self.original_form.arguments(), complex_mode) # Currently testing how fast this is
419
420 # TODO: This member is used by unit tests, change the tests to
/usr/lib/python3/dist-packages/ufl/algorithms/check_arities.py in check_form_arity(form, arguments, complex_mode)
175 def check_form_arity(form, arguments, complex_mode=False):
176 for itg in form.integrals():
--> 177 check_integrand_arity(itg.integrand(), arguments, complex_mode)
/usr/lib/python3/dist-packages/ufl/algorithms/check_arities.py in check_integrand_arity(expr, arguments, complex_mode)
157 key=lambda x: (x.number(), x.part())))
158 rules = ArityChecker(arguments)
--> 159 arg_tuples = map_expr_dag(rules, expr, compress=False)
160 args = tuple(a[0] for a in arg_tuples)
161 if args != arguments:
/usr/lib/python3/dist-packages/ufl/corealg/map_dag.py in map_expr_dag(function, expression, compress)
35 Return the result of the final function call.
36 """
---> 37 result, = map_expr_dags(function, [expression], compress=compress)
38 return result
39
/usr/lib/python3/dist-packages/ufl/corealg/map_dag.py in map_expr_dags(function, expressions, compress)
84 r = handlers[v._ufl_typecode_](v)
85 else:
---> 86 r = handlers[v._ufl_typecode_](v, *[vcache[u] for u in v.ufl_operands])
87
88 # Optionally check if r is in rcache, a memory optimization
/usr/lib/python3/dist-packages/ufl/algorithms/check_arities.py in sum(self, o, a, b)
46 def sum(self, o, a, b):
47 if a != b:
---> 48 raise ArityMismatch("Adding expressions with non-matching form arguments {0} vs {1}.".format(_afmt(a), _afmt(b)))
49 return a
50
ArityMismatch: Adding expressions with non-matching form arguments () vs ('v_1',).
When I write this (I remove the Kt from sigma(u)):
def sigma(u):
return (lambda_*nabla_div(u))*Identity(d) + (2*mu)*epsilon(u)
It works perfectly.
In this page (Click here), they try to plot the same kind problem and it works on my computer.
Do you know how to fix it ?
I had exactly the same question and a colleague of mine did figure it out for me. As there is no answer given here, I will try to leave some directions to guide others to the solution. I have not a lot of expertise yet, so please consider that my use of terminology might be a little bit off.
The error of fenics somewhat mislead me into thinking the error is in the definition of the stress term sigma. It is not exactly there. The right handside and the left handside in the solve function are not defined correctly (also shown in the very top of the error code). The term kT*Identity(d) in the stress function sigma, is not dependent on the trialfunction u. It is just multiplied by the testfunction v later (epsilon(v)). Therefore it has to go into the L of the equation of the solver.
Beneath the Link that you shared, the scipt uses the rhs and lhs function to correctly split the equation into a and L.
I am currently printing onto console, can I print the output onto a text file in tabular form?
I am trying to write the file with:
with open("increment.txt", "w") as file:
file.write(i, mol)
file.close()
Here is the program:
import numpy as np
i: int
for i in range(1, 100,5):
mol = int((i*5)/(i+2))
print('i & mol are:',i, mol)
with open("incre.txt", "w") as file:
file.write(i, mol)
file.close()
Error message..
file.write(i, mol)
TypeError: write() argument must be str, not tuple
You are defining mol inside of your loop; here is the corrected code as far as I understand your question:
with open("incre.txt", "w") as file:
for i in range(1, 100, 5):
mol = int((i * 5) / (i + 2))
file.write(str(i) + " " + str(mol))
file.close()
This will write your i variable separated by a space, then your mol variable. Note since you haven't specified an output directory, it will create the file wherever your python script is stored.
Here is a more pythonic approach:
def write_example(output_path):
with open(output_path, "w") as file:
for i in range(1, 100, 5):
mol = int((i * 5) / (i + 2))
file.write(str(i) + " " + str(mol) + "\n")
file.close()
outpath = "/home/some_path_here/test.txt"
write_example(outpath)
This produces a txt file with the following contents:
1 1
6 3
11 4
16 4
21 4
26 4
31 4
36 4
41 4
46 4
51 4
56 4
61 4
66 4
71 4
76 4
81 4
86 4
91 4
96 4
Each of these being on a new line.
Let me know if this helped! Cheers!
I want to apply my function (f1) to array of numbers (cdr_test) using multiprocessing. My code:
cdr_test = [x for x in range(0, 100000)]
def f1(el):
a = Counter() #make new vector for each cdr
for k,v in d3.items():
if el in v:
a = a + Counter(itertools.product([el], v))
return a
if __name__ == '__main__':
pool = mp.Pool(20)
results = pool.map(f1, cdr_test)
pool.close()
pool.join()
out = open('out.txt', 'w')
for result in results:
for k,v in result.items():
out.write('\t'.join(map(str,k))+"\t"+str(v)+"\n")
out.close()
pool.close()
I have 'cannot allocate memory'. If I use an array of smaller length (100), then everything works.
Stacktrace:
OSError Traceback (most recent call last)
<ipython-input-3-b8dc4a3d12b3> in <module>()
9
10 if __name__ == '__main__':
---> 11 pool = mp.Pool(1000)
12 results = pool.map(f1, cdr_test)
13 #new section
/home/fedorovaad/anaconda3/lib/python3.5/multiprocessing/context.py in Pool(self, processes, initializer, initargs, maxtasksperchild)
116 from .pool import Pool
117 return Pool(processes, initializer, initargs, maxtasksperchild,
--> 118 context=self.get_context())
119
120 def RawValue(self, typecode_or_type, *args):
/home/fedorovaad/anaconda3/lib/python3.5/multiprocessing/pool.py in __init__(self, processes, initializer, initargs, maxtasksperchild, context)
166 self._processes = processes
167 self._pool = []
--> 168 self._repopulate_pool()
169
170 self._worker_handler = threading.Thread(
/home/fedorovaad/anaconda3/lib/python3.5/multiprocessing/pool.py in _repopulate_pool(self)
231 w.name = w.name.replace('Process', 'PoolWorker')
232 w.daemon = True
--> 233 w.start()
234 util.debug('added worker')
235
/home/fedorovaad/anaconda3/lib/python3.5/multiprocessing/process.py in start(self)
103 'daemonic processes are not allowed to have children'
104 _cleanup()
--> 105 self._popen = self._Popen(self)
106 self._sentinel = self._popen.sentinel
107 _children.add(self)
/home/fedorovaad/anaconda3/lib/python3.5/multiprocessing/context.py in _Popen(process_obj)
265 def _Popen(process_obj):
266 from .popen_fork import Popen
--> 267 return Popen(process_obj)
268
269 class SpawnProcess(process.BaseProcess):
/home/fedorovaad/anaconda3/lib/python3.5/multiprocessing/popen_fork.py in __init__(self, process_obj)
18 sys.stderr.flush()
19 self.returncode = None
---> 20 self._launch(process_obj)
21
22 def duplicate_for_child(self, fd):
/home/fedorovaad/anaconda3/lib/python3.5/multiprocessing/popen_fork.py in _launch(self, process_obj)
65 code = 1
66 parent_r, child_w = os.pipe()
---> 67 self.pid = os.fork()
68 if self.pid == 0:
69 try:
OSError: [Errno 12] Cannot allocate memory
Are there ways to solve this?
The code you show is different from the one in the error.
---> 11 pool = mp.Pool(1000)
You are trying to spawn way too many processes, the OS will run out of memory before it can allocate them all.
You don't need this many processes to carry on your job, just use multiprocessing.cpu_count() to know how many CPUs your platform has and spawn a pool of that size.