Python Forum

Hi everyone,

I am trying to load the output of my model into matrix. Below is the model output:

[array([[3.3480644, 2.6733143, 2.3946035, 1.651863 , 1.4944869, 1.4064457,
         2.0166957, 2.7554176]], dtype=float32),
 array([[2.6761208 , 2.147566  , 1.2485328 , 0.6944433 , 0.87446946,
         1.1815729 , 1.879112  , 2.3758152 ]], dtype=float32),
 array([[2.3128846 , 1.2720771 , 0.9003729 , 0.92187667, 0.9644935 ,
         1.2748135 , 2.2232022 , 2.5829005 ]], dtype=float32),
 array([[1.9967098, 2.0049589, 1.0679721, 1.3402292, 2.1737716, 3.2332737,
         3.9912915, 4.124048 ]], dtype=float32),
 array([[1.0747261, 2.0689538, 3.204754 , 3.952746 , 5.636964 , 5.665282 ,
         4.85702  , 3.7058673]], dtype=float32),
 array([[0.9920357, 1.9206009, 2.5796123, 4.0336523, 5.374252 , 5.6010337,
         5.218882 , 4.5167184]], dtype=float32),
 array([[3.4540672, 4.065122 , 4.130976 , 4.1544843, 3.7034013, 3.9074   ,
         3.742108 , 2.6969736]], dtype=float32),
 array([[1.3718978, 2.7911968, 3.7615182, 5.0639954, 4.2722535, 3.632765 ,
         3.5549235, 2.8956242]], dtype=float32),
 array([[1.9010936, 2.4020395, 3.4859495, 3.2695432, 3.007401 , 2.263217 ,
         2.2069273, 1.3685739]], dtype=float32),
 array([[2.3215306 , 2.7285109 , 3.6797216 , 4.2266564 , 3.4872656 ,
         2.6049411 , 0.78771996, 0.6663305 ]], dtype=float32),
 array([[2.126574 , 3.1755157, 3.8428771, 5.507214 , 4.951423 , 3.8779044,
         3.7308226, 3.217761 ]], dtype=float32),
 array([[3.785448 , 4.4183755, 4.792441 , 5.056678 , 4.3832984, 4.204617 ,
         3.2348392, 2.3076746]], dtype=float32),
 array([[3.6433454, 3.9527392, 3.840207 , 3.5228324, 3.198389 , 2.898097 ,
         2.2972064, 1.6669679]], dtype=float32),
 array([[3.4212291, 3.6168365, 3.2267141, 3.4284039, 3.5152335, 3.36041  ,
         3.3625872, 2.1926944]], dtype=float32),
 array([[4.014863 , 4.2513857, 4.336287 , 4.214694 , 4.285961 , 3.636494 ,
         2.4518902, 1.901188 ]], dtype=float32),
 array([[3.7877383, 4.413838 , 5.1336374, 4.8160186, 4.048491 , 2.8648334,
         1.7532759, 1.0061626]], dtype=float32),
 array([[4.076805 , 4.0782094, 4.5997   , 4.610984 , 3.9340289, 2.4861271,
         1.4596449, 1.421582 ]], dtype=float32),
 array([[3.3311005, 3.1229572, 3.663089 , 3.2575672, 3.149508 , 2.0260038,
         2.0294707, 0.8982593]], dtype=float32),
 array([[4.253025 , 3.6342578, 3.6889012, 3.4614203, 2.7032697, 2.108417 ,
         1.8192803, 1.922114 ]], dtype=float32),
 array([[4.9552517 , 5.4846144 , 4.1990805 , 3.0923777 , 2.1978126 ,
         0.6762241 , 0.1946709 , 0.29030347]], dtype=float32),
 array([[3.8667407, 3.7753189, 3.4018555, 2.4203453, 2.8018296, 2.6180742,
         2.4156811, 2.4616084]], dtype=float32),
 array([[ 4.4066114 ,  4.488062  ,  4.170538  ,  3.3538008 ,  2.780805  ,
          2.0708318 ,  0.9603991 , -0.04771591]], dtype=float32),
 array([[3.369717 , 3.334411 , 2.4926825, 2.415385 , 1.5112386, 1.4150862,
         1.7556384, 1.8881752]], dtype=float32),
 array([[2.836572 , 2.9929788, 3.2093453, 3.1515193, 2.9640412, 2.755675 ,
         2.2785902, 2.261478 ]], dtype=float32),
 array([[3.6565807, 3.4455597, 3.568813 , 2.6705475, 2.1754317, 2.349119 ,
         2.1309204, 2.204011 ]], dtype=float32)]

I am using the below functions to do it:

def add_in_front(data, n):

    data_new = []

    for i in range(n):
        data_new.append([0][0][0])
    return data_new + data

def add_behind(data, n):

    data_new = data

    for i in range(n):
        data_new.append([0][0][0])
    return data_new

def sum_row(data_row):

    count = 0
    total = 0
    for data in data_row:
        total += data
        if data != 0:
            count += 1

    return total / count

And my code to load it is:

overall = []
infront = 0
behind = 24


for calculation in calculations:

    calculation = add_in_front(calculation, infront)
    calculation = add_behind(calculation, behind)

    overall.append(calculation)

    infront += 1
    behind -= 1

projection = np.transpose(overall)[0]

matrix_final = []

for row in projection:
    matrix_final.append(round(sum_row(row), 2))


for element in matrix_final:
    print(element)

I am getting the following error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-18-774883fae2e5> in <module>
      6 for calculation in calculations:
      7 
----> 8     calculation = add_in_front(calculation, infront)
      9     calculation = add_behind(calculation, behind)
     10 

<ipython-input-15-aadb2e2a0eb1> in add_in_front(data, n)
      5     for i in range(n):
      6         data_new.append([0][0][0])
----> 7     return data_new + data
      8 
      9 def add_behind(data, n):

ValueError: operands could not be broadcast together with shapes (0,) (1,8)

The functions were developed on lists not arrays and I think that might be the issue. I tried converting my arrays to lists with the tolist() function from numpy but it just said there was no such object:

---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-63dbe332ed6e> in <module>
79 behind = 24
80
---> 81 calculations = calculations.tolist()
82
83 for calculation in calculations:

AttributeError: 'list' object has no attribute 'tolist'

Can anyone help me solve the error and get the functions to work on my model array output?

Thanks
Scott

Not sure to figure out exactly what you want to do, but I guess the following code may help you. Note that I think it's better to convert directly the list:

import numpy as np

List = [[3.3480644 , 2.6733143 , 2.3946035 , 1.651863  , 1.4944869  , 1.4064457 , 2.0166957 , 2.7554176 ], 
        [2.6761208 , 2.147566  , 1.2485328 , 0.6944433 , 0.87446946 , 1.1815729 , 1.879112  , 2.3758152 ]]

List2Array = np.asarray(List)       # convert a list into a Numpy array
print(f"List2Array={List2Array}\n")

r, c = np.shape(List2Array)         # r=number of rows and c=number of columns

# to add zeros in front
M1 = np.zeros((r, 1), dtype=np.float32)
InFront = np.hstack((M1, List2Array))
print(f"InFront={InFront}\n")

# to add zeros at the bottom
M2 = np.zeros((1, c), dtype=np.float32)
AtBottom = np.vstack((List2Array, M2))
print(f"AtBottom={AtBottom}\n")

# to sum the array per row
SumPerRow = np.sum(List2Array, axis=1)
print(f"SumPerRow={SumPerRow}\n")

# to sum the array per column
SumPercolumn = np.sum(List2Array, axis=0)
print(f"SumPercolumn={SumPercolumn}\n")

# to eventually sum all terms
SumAllTerms = np.sum(List2Array)
print(f"SumAllTerms={SumAllTerms}")

Output:List2Array=[[3.3480644  2.6733143  2.3946035  1.651863   1.4944869  1.4064457
  2.0166957  2.7554176 ]
 [2.6761208  2.147566   1.2485328  0.6944433  0.87446946 1.1815729
  1.879112   2.3758152 ]]

InFront=[[0.         3.3480644  2.6733143  2.3946035  1.651863   1.4944869
  1.4064457  2.0166957  2.7554176 ]
 [0.         2.6761208  2.147566   1.2485328  0.6944433  0.87446946
  1.1815729  1.879112   2.3758152 ]]

AtBottom=[[3.3480644  2.6733143  2.3946035  1.651863   1.4944869  1.4064457
  2.0166957  2.7554176 ]
 [2.6761208  2.147566   1.2485328  0.6944433  0.87446946 1.1815729
  1.879112   2.3758152 ]
 [0.         0.         0.         0.         0.         0.
  0.         0.        ]]

SumPerRow=[17.7408911  13.07763246]

SumPercolumn=[6.0241852  4.8208803  3.6431363  2.3463063  2.36895636 2.5880186
 3.8958077  5.1312328 ]

SumAllTerms=30.81852356

Scott

paul18fr