Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here is the implementation of the strides() function: 

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import numpy as np

def strides(T):
    window_size = 50
    stride = 10
    num_strides = (len(T) - window_size) // stride + 1
    output = np.zeros((num_strides, window_size), dtype=T.dtype)

    for i in range(num_strides):
        start = i * stride
        end = start + window_size
        output[i] = T[start:end]

    return output

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
result = strides(T)

for i, arr in enumerate(result):
    print(f"Stride {i+1}: {arr}")
You can replace the example array `T` with your own numpy array of tokens. The function `strides()` will return an array of arrays, each containing a strided subset of the original array.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T):
    window_size = 50
    stride = 10
    num_strides = (len(T) - window_size) // stride + 1
    output = np.zeros((num_strides, window_size), dtype=T.dtype)

    for i in range(num_strides):
        start = i * stride
        end = start + window_size
        output[i] = T[start:end]

    return output

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
result = strides(T)

for i, arr in enumerate(result):
    print(f"Stride {i+1}: {arr}")

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: False

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(T):
    window_size = 50
    stride = 10
    num_strides = (len(T) - window_size) // stride + 1
    output = np.zeros((num_strides, window_size), dtype=T.dtype)

    for i in range(num_strides):
        start = i * stride
        end = start + window_size
        output[i] = T[start:end]

    return output

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
result = strides(T)

for i, arr in enumerate(result):
    print(f"Stride {i+1}: {arr}")





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 19, in <module> result = strides(T) ^^^^^^^^^^ File "/usr/src/app/main.py", line 8, in strides output = np.zeros((num_strides, window_size), dtype=T.dtype) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ValueError: negative dimensions are not allowed

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Final Answer: False

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

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import numpy as np

def strides(T):
    num_strides = len(T) - 50
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50)

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
result = strides(T)
print(result)
This function uses the `sliding_window_view` function from numpy's stride_tricks module to create a view of the input array `T` with a window shape of (50,). It then reshapes the view to have `num_strides` rows and 50 columns, effectively creating the desired array of strided windows.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50)

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
result = strides(T)
print(result)

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: False

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50)

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
result = strides(T)
print(result)





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 10, in <module> result = strides(T) ^^^^^^^^^^ File "/usr/src/app/main.py", line 6, in strides return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/numpy/lib/stride_tricks.py", line 332, in sliding_window_view raise ValueError( ValueError: window shape cannot be larger than input array shape

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Final Answer: False

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here is the implementation of the strides() function: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50 + 1
    result = np.lib.stride_tricks.as_strided(T, shape=(num_strides, 50), strides=(T.itemsize, T.itemsize))
    return result

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
result = strides(T)
print(result)
This function uses the `np.lib.stride_tricks.as_strided()` function to create a view of the input array `T` with a sliding window of size 50. The `shape` parameter specifies the shape of the output array, and the `strides` parameter specifies the number of bytes to move in each dimension to get to the next element in that dimension. The function will return an array of shape `(n, 50)` where `n` is the number of strides that can be taken along the input array `T`. Each row of the output array will be a slice of the input array starting from index `0`, `10`, `20`, and so on.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50 + 1
    result = np.lib.stride_tricks.as_strided(T, shape=(num_strides, 50), strides=(T.itemsize, T.itemsize))
    return result

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
result = strides(T)
print(result)

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50 + 1
    result = np.lib.stride_tricks.as_strided(T, shape=(num_strides, 50), strides=(T.itemsize, T.itemsize))
    return result

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
result = strides(T)
print(result)





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 11, in <module> result = strides(T) ^^^^^^^^^^ File "/usr/src/app/main.py", line 6, in strides result = np.lib.stride_tricks.as_strided(T, shape=(num_strides, 50), strides=(T.itemsize, T.itemsize)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/numpy/lib/stride_tricks.py", line 105, in as_strided array = np.asarray(DummyArray(interface, base=x)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ValueError: negative dimensions are not allowed

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Final Answer: False

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here is the implementation of the strides() function: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50
    return np.array([T[i:i+50] for i in range(0, num_strides, 10)])

# Example Usage
T = np.array([i for i in range(100)])
result = strides(T)
print(result)
This function takes an input array T and creates a new array by striding through the original array with a window size of 50 and a stride of 10. The function returns a numpy array containing the strided subarrays.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50
    return np.array([T[i:i+50] for i in range(0, num_strides, 10)])

# Example Usage
T = np.array([i for i in range(100)])
result = strides(T)
print(result)

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: False

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50
    return np.array([T[i:i+50] for i in range(0, num_strides, 10)])

# Example Usage
T = np.array([i for i in range(100)])
result = strides(T)
print(result)





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: [[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] [30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79] [40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89]] Traceback (most recent call last): File "/usr/src/app/main.py", line 19, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got (5, 50) instead of (6, 50)

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Final Answer: False

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

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import numpy as np

def strides(T):
    num_strides = len(T) - 50 + 1
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50)

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
result = strides(T)
print(result)
This function `strides()` uses NumPy's `sliding_window_view` function to create a view of the input array `T` with a sliding window of size 50. It then reshapes the view to get the desired output of `[T[0:50], T[10:60], T[20:70], ...]`.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50 + 1
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50)

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
result = strides(T)
print(result)

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: False

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(T):
    num_strides = len(T) - 50 + 1
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50)

# Example usage
T = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
result = strides(T)
print(result)





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 10, in <module> result = strides(T) ^^^^^^^^^^ File "/usr/src/app/main.py", line 6, in strides return np.lib.stride_tricks.sliding_window_view(T, window_shape=(50,)).reshape(num_strides, 50) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/numpy/lib/stride_tricks.py", line 332, in sliding_window_view raise ValueError( ValueError: window shape cannot be larger than input array shape

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Final Answer: False