This is my code
import wolframalpha
app_id = '876P8Q-R2PY95YEXY'
client = wolframalpha.Client(app_id)
res = client.query(input('Question: '))
print(next(res.results).text)
the question I tried was 1 + 1
and i run it and then i get this error
Traceback (most recent call last):
File "c:/Users/akshi/Desktop/Xander/Untitled.py", line 9, in <module>
print(next(res.results).text)
File "C:\Users\akshi\AppData\Local\Programs\Python\Python38\lib\site-packages\wolframalpha\__init__.py", line 166, in text
return next(iter(self.subpod)).plaintext
ValueError: dictionary update sequence element #0 has length 1; 2 is required
Please help me
I was getting the same error when I tried to run the same code.
You can refer to "Implementing Wolfram Alpha Search" section of this website for better understanding of how the result was extracted from the dictionary returned.
https://medium.com/#salisuwy/build-an-ai-assistant-with-wolfram-alpha-and-wikipedia-in-python-d9bc8ac838fe
Also, I tried the following code by referring to the above website....hope it might help you :)
import wolframalpha
client = wolframalpha.Client('<your app_id>')
query = str(input('Question: '))
res = client.query(query)
if res['#success']=='true':
pod0=res['pod'][0]['subpod']['plaintext']
print(pod0)
pod1=res['pod'][1]
if (('definition' in pod1['#title'].lower()) or ('result' in pod1['#title'].lower()) or (pod1.get('#primary','false') == 'true')):
result = pod1['subpod']['plaintext']
print(result)
else:
print("No answer returned")
I am relatively new to python, and as such I don't always understand why I get errors. I keep getting this error:
Traceback (most recent call last):
File "python", line 43, in <module>
ValueError: invalid literal for int() with base 10: 'O'
This is the line it's referring to:
np.insert(arr, [i,num], "O")
I'm trying to change a value in a numpy array.
Some code around this line for context:
hOne = [one,two,three]
hTwo = [four,five,six]
hThree = [seven, eight, nine]
arr = np.array([hOne, hTwo, hThree])
test = "O"
while a != Answer :
Answer = input("Please Enter Ready to Start")
if a == Answer:
while win == 0:
for lists in arr:
print(lists)
place = int(input("Choose a number(Use arabic numerals 1,5 etc.)"))
for i in range(0,len(arr)):
for num in range(0, len(arr[i])):
print(arr[i,num], "test")
print(arr)
if place == arr[i,num]:
if arr[i,num]:
np.delete(arr, [i,num])
np.insert(arr, [i,num], "O")
aiTurn = 1
else:
print(space_taken)
The number variables in the lists just hold the int version of themselves, so one = 1, two = 2 three = 3, etc
I've also tried holding "O" as a variable and changing it that way as well.
Can anyone tell me why I'm getting this error?
I'm trying to implement a dynamic_rnn_decoder. However, I get an exception, because after the second element the Tensors in the cell are already created. Thus I want to set reuse=True after the first iteration.
Is there a op which calls dynamically a function depending on a condition (like fn_dyn = tf.cond(cond, fn1, fn2))
Hence, I want to implement this dynamically:
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
The a simplified _time_step-function for _dynamic_rnn_loop could be something like that:
def _time_step(time, output_ta_t, *state):
input_t = input_ta.read(time)
# Restore some shape information
input_t.set_shape([const_batch_size, const_depth])
# Pack state back up for use by cell
state = (_packed_state(structure=state_size, state=state)
if state_is_tuple else state[0])
def call_with_previous(feed_previous_t):
if feed_previous_t:
prev = output_ta_t.read(time - 1)
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0], output_projection[1])
cell_input = math_ops.reduce_max(prev, 1)
print(cell_input.get_shape())
cell_input.set_shape([const_batch_size, const_depth])
else:
cell_input = input_t
def call_cell_t(cell_input_t, state_t):
# set ruse after first call
output_t, state_t = cell(cell_input_t, state_t)
variable_scope.get_variable_scope().reuse_variables()
return output_t, state_t
return lambda: call_cell_t(cell_input, state)
# >>> doesn't work
call_cell = tf.cond(tf.equal(time, tf.constant(0, dtype=tf.int32)),
call_with_previous(False),
call_with_previous(True))
if sequence_length is not None:
(output, new_state) = _rnn_step(
time=time,
sequence_length=sequence_length,
min_sequence_length=min_sequence_length,
max_sequence_length=max_sequence_length,
zero_output=zero_output,
state=state,
call_cell=call_cell,
state_size=state_size,
skip_conditionals=True)
else:
(output, new_state) = call_cell()
# Pack state if using state tuples
new_state = (tuple(_unpacked_state(new_state)) if state_is_tuple else (new_state,))
output_ta_t = output_ta_t.write(time, output)
return (time + 1, output_ta_t) + new_state
Thanks, cheers!
while_loop only calls the underlying body function once. not dynamically for every time step. if you're getting an error when getting the variable, it's because you also access the variable elsewhere in your code.
In this case, looks like it's because of your cond statement. this causes two calls to cell(). Try to factor this so the cell call is outside the cond.
Alternatively, as a hack, have the cell call inside a try except block. If you get a variable access error, just set reuse variable and call it again.
source: i wrote dynamic_rnn.
Strange error from numpy via matplotlib when trying to get a histogram of a tiny toy dataset. I'm just not sure how to interpret the error, which makes it hard to see what to do next.
Didn't find much related, though this nltk question and this gdsCAD question are superficially similar.
I intend the debugging info at bottom to be more helpful than the driver code, but if I've missed something, please ask. This is reproducible as part of an existing test suite.
if n > 1:
return diff(a[slice1]-a[slice2], n-1, axis=axis)
else:
> return a[slice1]-a[slice2]
E TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U1') dtype('<U1') dtype('<U1')
../py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py:1567: TypeError
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(1567)diff()
-> return a[slice1]-a[slice2]
(Pdb) bt
[...]
py2.7.11-venv/lib/python2.7/site-packages/matplotlib/axes/_axes.py(5678)hist()
-> m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(606)histogram()
-> if (np.diff(bins) < 0).any():
> py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(1567)diff()
-> return a[slice1]-a[slice2]
(Pdb) p numpy.__version__
'1.11.0'
(Pdb) p matplotlib.__version__
'1.4.3'
(Pdb) a
a = [u'A' u'B' u'C' u'D' u'E']
n = 1
axis = -1
(Pdb) p slice1
(slice(1, None, None),)
(Pdb) p slice2
(slice(None, -1, None),)
(Pdb)
I got the same error, but in my case I am subtracting dict.key from dict.value. I have fixed this by subtracting dict.value for corresponding key from other dict.value.
cosine_sim = cosine_similarity(e_b-e_a, w-e_c)
here I got error because e_b, e_a and e_c are embedding vector for word a,b,c respectively. I didn't know that 'w' is string, when I sought out w is string then I fix this by following line:
cosine_sim = cosine_similarity(e_b-e_a, word_to_vec_map[w]-e_c)
Instead of subtracting dict.key, now I have subtracted corresponding value for key
I had a similar issue where an integer in a row of a DataFrame I was iterating over was of type numpy.int64. I got the
TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U1') dtype('<U1') dtype('<U1')
error when trying to subtract a float from it.
The easiest fix for me was to convert the row using pd.to_numeric(row).
Why is it applying diff to an array of strings.
I get an error at the same point, though with a different message
In [23]: a=np.array([u'A' u'B' u'C' u'D' u'E'])
In [24]: np.diff(a)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-24-9d5a62fc3ff0> in <module>()
----> 1 np.diff(a)
C:\Users\paul\AppData\Local\Enthought\Canopy\User\lib\site-packages\numpy\lib\function_base.pyc in diff(a, n, axis)
1112 return diff(a[slice1]-a[slice2], n-1, axis=axis)
1113 else:
-> 1114 return a[slice1]-a[slice2]
1115
1116
TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray'
Is this a array the bins parameter? What does the docs say bins should be?
I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue. I was trying to concatenate rather than take the difference but I think the principle is the same here. I provided a similar answer on another question so I hope that is OK.
In essence you need to use a different data type cast, in my case I needed str not float, I suspect yours is the same so my suggested solution is. I am sorry I cannot test it before suggesting but I am unclear from your example what you were doing.
return diff(str(a[slice1])-str(a[slice2]), n-1, axis=axis)
Please see my example code below for the fix to my code, the change occurs on the third to last line. The code is to produce a basic random forest model.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
This leads to an error of;
Traceback (most recent call last):
File "min_example.py", line 40, in <module>
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
These examples are from a larger code so I hope the examples are clear enough.
I think #James is right. I got stuck by same error while working on Polyval(). And yeah solution is to use the same type of variabes. You can use typecast to cast all variables in the same type.
BELOW IS A EXAMPLE CODE
import numpy
P = numpy.array(input().split(), float)
x = float(input())
print(numpy.polyval(P,x))
here I used float as an output type. so even the user inputs the INT value (whole number). the final answer will be typecasted to float.
I ran into the same issue, but in my case it was just a Python list instead of a Numpy array used. Using two Numpy arrays solved the issue for me.
I am having troubles with my money variable. Each time I save and run, a ERROR pops up and I try to fix the problem but can't figure out how? I really need a reply so please soon. This is my project:
money = 0.0
Chop = 0
spike = 0
name = input("What is your name?")
greeting = 'Hello ' + name + ','
def Command():
Command = input('Press \"C\" to continue or Press \"E\" to exit -->')
if name == 'SpikeTheKing':
print('Welcome Spike,')
spike = 1
elif name == 'Spike':
spike = 1
print ('Welcome Spike,')
else:
print(greeting)
def draw_line():
print ('----------------')
def Work1():
Chop = input('Type anything to earn $1 -->')
while Chop == ('C') or ('c'):
print ('$1 earned')
money = money+1
displayMoney()
Chop = 0
Command()
def displayMoney():
draw_line()
print(('Money = '),('$'),(money))
draw_line()
displayMoney()
Work1()
if Command == ('C'):
Work1()
if Command == ('E'):
quit()
Each time I save and run this happens:
Traceback (most recent call last):
File "C:\Users\Leora\Desktop\Python Files\Python.py", line 35, in <module>
Work1()
File "C:\Users\Leora\Desktop\Python Files\Python.py", line 26, in Work1
money = money+1
UnboundLocalError: local variable 'money' referenced before assignment
What should I change?
In the def Work code, before you do money = money+1, you will need to initialize money. So in the first statement after def Work, do: money = 0...
Alternatively, money is global , so you could do global money, in the first line after def Work instead of money=0....
The global keyword tells the interpreter, that money is defined outside the scope of Work function and to look for it there