I want to take a list and use enumerate to easily get the index and value of each element in the list. For some reason it keeps giving me the same error. I'm pretty new to Python, but not remotely new to coding. I would appreciate any insight.Ultimately, I'm calculating a heuristic for a hill climbing algorithm. But I'd like to handle the rest on my own and this is the only part that is giving me trouble right now.
def getHeuristic(state, graph):
heur = 0
for c, item in enumerate(state):
print(item)
vert = graph[item]
for i in vert:
if i in state:
if i >= c:
heur += vert[i]
return heur
And this is the error:
line 32, in getHeuristic
for c, item in enumerate(state):
TypeError: 'int' object is not iterable
Related
I'm trying to compare each element of one array to each element in another one. The arrays are fairly large -
arr1.shape = (59913, )
arr2.shape = (988114, )
To do this comparison, I use the following code:
A = np.array(arr1[:])[:, np.newaxis] == np.array(arr1[:])[np.newaxis, :]
np.sum(A)
The strange thing is that when i limit the length of either of arr1 or arr2 - everything works as expected and i get a NxN boolean matrix as A, but when I try to run it for the full arrays - i get the following warning -
DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
and a single output: False
Any help would be highly appreciated.
Rather late, but I just encountered the same error and I found out that it is due to lack of memory.
res = np.where(~((a[:, None] == b).all(-1).any(-1)))
a.shape = (14731, 128) b.shape = (3725, 128)
main__:5: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
Traceback (most recent call last):
AttributeError: 'bool' object has no attribute 'all'
That's what I got. By reducing the size of b gradually, I realized that at some point starts working properly.
So, I think it is a matter of memory lack and the message displayed is rather misleading.
My goal was to keep rows of a that are not present in b. Found a solution using pandas.
I have a dataframe containing astronomical data:
I'm using statsmodels.formula.api to try to apply a polynomial fit to an dataframe, using columns labelled log_z and U, B, V, and other variables. I've got so far
sources['log_z'] = np.log10(sources.z)
mask = ~np.isnan((B-I)) & ~np.isnan(log_z)
model = ols(formula='(B-I) + np.power((U-R),2) ~ log_z', data = [log_z[mask], (B-I)[mask]]).fit()
but I keep getting
PatsyError: Error evaluating factor: TypeError: list indices must be integers or slices, not str
(B-I) + np.power((U-R),2) ~ log_z
^^^^^^^^^^^^^^^^^
even though I'm passing arrays into the function. I get the same error message (apart from the last line) no matter what arrays I use, or how I format them. Can anyone see what I'm doing wrong?
I'm trying to use the mx.nd.onehot_encode function, which should be straightforward, but I'm getting errors that are difficult to parse. Here is the example usage I'm trying.
m0 = mx.nd.zeros(15)
mx.nd.onehot_encode(mx.nd.array([0]), m0)
I expect this to return a 15 dim vector (at same address as m0) with only the first element set to 1. Instead I get the error:
src/ndarray/./ndarray_function.h:73: Check failed: index.ndim() == 1 && proptype.ndim() == 2 OneHotEncode only support 1d index.
Neither ndarray is of dimension 2, so why am I getting this error? Is there some other input format I should be using?
It seems that mxnet.ndarray.onehot_encode requires the target ndarray to explicitly have the shape [1, X].
I tried:
m0 = mx.nd.zeros((1, 15))
mx.nd.onehot_encode(mx.nd.array([0]), m0)
It reported no error.
Im trying to use findnext on an array of vectors of vectors to be used for coords of other nodes connected to the indexed node in a neural net. findfirst works fine, but findnext causes a crash. I there an easy solution?
x = fill!(Array(Vector{Vector{Int64}},5,5,5),[])
push!(x[1,1,1],[1,1])
push!(x[1,1,1],[1,2])
f = findfirst(x[1,1,1],[1,3])
n = findnext(x[1,1,1],[1,3]) #Crash
I am using Julia v0.3.5 and this is the error message I see:
julia> n = findnext(x[1,1,1],[1,3]) #Crash
ERROR: `findnext` has no method matching findnext(::Array{Array{Int64,1},1}, ::Array{Int64,1})
?findnext reveals that it requires three arguments, A, v, start::Integer. The start is the index to start looking for the element:
julia> n = findnext(x[1,1,1],[1,3], 1)
0
I am pretty new to Pandas and trying to find out where my code breaks. Say, I am doing a type conversion:
df['x']=df['x'].astype('int')
...and I get an error "ValueError: invalid literal for long() with base 10: '1.0692e+06'
In general, if I have 1000 entries in the dataframe, how can I find out what entry causes a break. Is there anything in ipdb to output the current location (i.e. where the code broke)? Basically, I am trying to pinpoint what value cannot be converted to Int.
The error you are seeing might be due to the value(s) in the x column being strings:
In [15]: df = pd.DataFrame({'x':['1.0692e+06']})
In [16]: df['x'].astype('int')
ValueError: invalid literal for long() with base 10: '1.0692e+06'
Ideally, the problem can be avoided by making sure the values stored in the
DataFrame are already ints not strings when the DataFrame is built.
How to do that depends of course on how you are building the DataFrame.
After the fact, the DataFrame could be fixed using applymap:
import ast
df = df.applymap(ast.literal_eval).astype('int')
but calling ast.literal_eval on each value in the DataFrame could be slow, which is why fixing the problem from the beginning is the best alternative.
Usually you could drop to a debugger when an exception is raised to inspect the problematic value of row.
However, in this case the exception is happening inside the call to astype, which is a thin wrapper around C-compiled code. The C-compiled code is doing the looping through the values in df['x'], so the Python debugger is not helpful here -- it won't allow you to introspect on what value the exception is being raised from within the C-compiled code.
There are many important parts of Pandas and NumPy written in C, C++, Cython or Fortran, and the Python debugger will not take you inside those non-Python pieces of code where the fast loops are handled.
So instead I would revert to a low-brow solution: iterate through the values in a Python loop and use try...except to catch the first error:
df = pd.DataFrame({'x':['1.0692e+06']})
for i, item in enumerate(df['x']):
try:
int(item)
except ValueError:
print('ERROR at index {}: {!r}'.format(i, item))
yields
ERROR at index 0: '1.0692e+06'
I hit the same problem, and as I have a big input file (3 million rows), enumerating all rows will take a long time. Therefore I wrote a binary-search to locate the offending row.
import pandas as pd
import sys
def binarySearch(df, l, r, func):
while l <= r:
mid = l + (r - l) // 2;
result = func(df, mid, mid+1)
if result:
# Check if we hit exception at mid
return mid, result
result = func(df, l, mid)
if result is None:
# If no exception at left, ignore left half
l = mid + 1
else:
r = mid - 1
# If we reach here, then the element was not present
return -1
def check(df, start, end):
result = None
try:
# In my case, I want to find out which row cause this failure
df.iloc[start:end].uid.astype(int)
except Exception as e:
result = str(e)
return result
df = pd.read_csv(sys.argv[1])
index, result = binarySearch(df, 0, len(df), check)
print("index: {}".format(index))
print(result)
To report all rows which fails to map due to any exception:
df.apply(my_function) # throws various exceptions at unknown rows
# print Exceptions, index, and row content
for i, row in enumerate(df):
try:
my_function(row)
except Exception as e:
print('Error at index {}: {!r}'.format(i, row))
print(e)