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

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes an array of tokens and returns a list of overlapping
  sub-arrays using the strided trick.

  Args:
    T: A NumPy array of tokens.
    window_size: The size of each sub-array (default: 50).
    step_size: The step size between the start indices of consecutive sub-arrays 
               (default: 10).

  Returns:
    A list of NumPy arrays, where each array is a sub-array of T.
  """

  stride_shape = ((len(T) - window_size) // step_size + 1, window_size)
  strides = (T.strides[0] * step_size, T.strides[0])
  return np.lib.stride_tricks.as_strided(T, shape=stride_shape, strides=strides)

# Example usage:
tokens = np.array(["token_" + str(i) for i in range(100)])
result = strides(tokens)
print(result)

**Explanation:** 1. **Import numpy:** We start by importing the NumPy library. 2. **Define the `strides` function:** - It takes three arguments: - `T`: The input array of tokens. - `window_size`: The desired size of each sub-array (defaulting to 50). - `step_size`: The step size between the starting indices of consecutive sub-arrays (defaulting to 10). 3. **Calculate `stride_shape`:** This tuple defines the shape of the resulting strided array. - The first element calculates the number of sub-arrays that will fit given the window and step size. - The second element is simply the `window_size`, as each sub-array will have this length. 4. **Calculate `strides`:** This tuple defines how to move through the original array's memory to create the strided view. - The first element (`T.strides[0] * step_size`) determines the byte jump needed to move to the next sub-array's starting element. - The second element (`T.strides[0]`) determines the byte jump needed to move to the next element within a sub-array. 5. **Use `np.lib.stride_tricks.as_strided`:** This is the core of the strided trick. It creates a new view of the original data (`T`) without copying it, using the calculated `shape` and `strides`. 6. **Return the strided array:** The function returns the newly created strided array, which effectively represents the overlapping sub-arrays. **Key points:** - **Efficiency:** The strided trick is very memory-efficient because it avoids copying the original data. It creates a new view that shares the same memory space. - **Use with caution:** Modifying the strided array will modify the original data, as they share the same memory. If you need to modify the sub-arrays independently, create a copy first. - **Versatility:** The strided trick can be used for various purposes beyond this specific example, such as creating rolling windows, sliding windows, and more.

Extract Code

I extracted the following code from that output:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes an array of tokens and returns a list of overlapping
  sub-arrays using the strided trick.

  Args:
    T: A NumPy array of tokens.
    window_size: The size of each sub-array (default: 50).
    step_size: The step size between the start indices of consecutive sub-arrays 
               (default: 10).

  Returns:
    A list of NumPy arrays, where each array is a sub-array of T.
  """

  stride_shape = ((len(T) - window_size) // step_size + 1, window_size)
  strides = (T.strides[0] * step_size, T.strides[0])
  return np.lib.stride_tricks.as_strided(T, shape=stride_shape, strides=strides)

# Example usage:
tokens = np.array(["token_" + str(i) for i in range(100)])
result = strides(tokens)
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:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes an array of tokens and returns a list of overlapping
  sub-arrays using the strided trick.

  Args:
    T: A NumPy array of tokens.
    window_size: The size of each sub-array (default: 50).
    step_size: The step size between the start indices of consecutive sub-arrays 
               (default: 10).

  Returns:
    A list of NumPy arrays, where each array is a sub-array of T.
  """

  stride_shape = ((len(T) - window_size) // step_size + 1, window_size)
  strides = (T.strides[0] * step_size, T.strides[0])
  return np.lib.stride_tricks.as_strided(T, shape=stride_shape, strides=strides)

# Example usage:
tokens = np.array(["token_" + str(i) for i in range(100)])
result = strides(tokens)
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:

[['token_0' 'token_1' 'token_2' 'token_3' 'token_4' 'token_5' 'token_6'
  'token_7' 'token_8' 'token_9' 'token_10' 'token_11' 'token_12'
  'token_13' 'token_14' 'token_15' 'token_16' 'token_17' 'token_18'
  'token_19' 'token_20' 'token_21' 'token_22' 'token_23' 'token_24'
  'token_25' 'token_26' 'token_27' 'token_28' 'token_29' 'token_30'
  'token_31' 'token_32' 'token_33' 'token_34' 'token_35' 'token_36'
  'token_37' 'token_38' 'token_39' 'token_40' 'token_41' 'token_42'
  'token_43' 'token_44' 'token_45' 'token_46' 'token_47' 'token_48'
  'token_49']
 ['token_10' 'token_11' 'token_12' 'token_13' 'token_14' 'token_15'
  'token_16' 'token_17' 'token_18' 'token_19' 'token_20' 'token_21'
  'token_22' 'token_23' 'token_24' 'token_25' 'token_26' 'token_27'
  'token_28' 'token_29' 'token_30' 'token_31' 'token_32' 'token_33'
  'token_34' 'token_35' 'token_36' 'token_37' 'token_38' 'token_39'
  'token_40' 'token_41' 'token_42' 'token_43' 'token_44' 'token_45'
  'token_46' 'token_47' 'token_48' 'token_49' 'token_50' 'token_51'
  'token_52' 'token_53' 'token_54' 'token_55' 'token_56' 'token_57'
  'token_58' 'token_59']
 ['token_20' 'token_21' 'token_22' 'token_23' 'token_24' 'token_25'
  'token_26' 'token_27' 'token_28' 'token_29' 'token_30' 'token_31'
  'token_32' 'token_33' 'token_34' 'token_35' 'token_36' 'token_37'
  'token_38' 'token_39' 'token_40' 'token_41' 'token_42' 'token_43'
  'token_44' 'token_45' 'token_46' 'token_47' 'token_48' 'token_49'
  'token_50' 'token_51' 'token_52' 'token_53' 'token_54' 'token_55'
  'token_56' 'token_57' 'token_58' 'token_59' 'token_60' 'token_61'
  'token_62' 'token_63' 'token_64' 'token_65' 'token_66' 'token_67'
  'token_68' 'token_69']
 ['token_30' 'token_31' 'token_32' 'token_33' 'token_34' 'token_35'
  'token_36' 'token_37' 'token_38' 'token_39' 'token_40' 'token_41'
  'token_42' 'token_43' 'token_44' 'token_45' 'token_46' 'token_47'
  'token_48' 'token_49' 'token_50' 'token_51' 'token_52' 'token_53'
  'token_54' 'token_55' 'token_56' 'token_57' 'token_58' 'token_59'
  'token_60' 'token_61' 'token_62' 'token_63' 'token_64' 'token_65'
  'token_66' 'token_67' 'token_68' 'token_69' 'token_70' 'token_71'
  'token_72' 'token_73' 'token_74' 'token_75' 'token_76' 'token_77'
  'token_78' 'token_79']
 ['token_40' 'token_41' 'token_42' 'token_43' 'token_44' 'token_45'
  'token_46' 'token_47' 'token_48' 'token_49' 'token_50' 'token_51'
  'token_52' 'token_53' 'token_54' 'token_55' 'token_56' 'token_57'
  'token_58' 'token_59' 'token_60' 'token_61' 'token_62' 'token_63'
  'token_64' 'token_65' 'token_66' 'token_67' 'token_68' 'token_69'
  'token_70' 'token_71' 'token_72' 'token_73' 'token_74' 'token_75'
  'token_76' 'token_77' 'token_78' 'token_79' 'token_80' 'token_81'
  'token_82' 'token_83' 'token_84' 'token_85' 'token_86' 'token_87'
  'token_88' 'token_89']
 ['token_50' 'token_51' 'token_52' 'token_53' 'token_54' 'token_55'
  'token_56' 'token_57' 'token_58' 'token_59' 'token_60' 'token_61'
  'token_62' 'token_63' 'token_64' 'token_65' 'token_66' 'token_67'
  'token_68' 'token_69' 'token_70' 'token_71' 'token_72' 'token_73'
  'token_74' 'token_75' 'token_76' 'token_77' 'token_78' 'token_79'
  'token_80' 'token_81' 'token_82' 'token_83' 'token_84' 'token_85'
  'token_86' 'token_87' 'token_88' 'token_89' 'token_90' 'token_91'
  'token_92' 'token_93' 'token_94' 'token_95' 'token_96' 'token_97'
  'token_98' 'token_99']]
All tests passed

Substring Evaluation

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

Final Answer: True

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

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes a NumPy array and returns a list of overlapping subarrays
  using the strided trick.

  Args:
    T: A 1-dimensional NumPy array of tokens.
    window_size: The desired size of each subarray (default: 50).
    step_size: The stride or step size between the starting indices of 
               consecutive subarrays (default: 10).

  Returns:
    A list of NumPy arrays, where each array is a subarray of T with the 
    specified window_size and overlapping by (window_size - step_size) elements. 
  """

  stride_shape = (len(T) - window_size + 1, window_size)
  strides = (T.strides[0] * step_size, T.strides[0])
  return np.lib.stride_tricks.as_strided(T, stride_shape, strides)

# Example usage
T = np.array(range(100))
result = strides(T)
print(result) 

**Explanation:** 1. **Import numpy:** We import the NumPy library for array manipulation. 2. **Define the strides function:** - It takes the input array `T`, `window_size`, and `step_size` as arguments. - **Calculate `stride_shape`:** This tuple determines the shape of the output array. It's calculated as `(number of subarrays, window_size)`. - The number of subarrays is determined by how many times the window can "slide" over the input array with the given step size. - **Calculate `strides`:** This tuple controls how the strided view moves through the original data in memory. - The first element is the stride in bytes to move to the next subarray (which is `step_size` * the size of each element in the original array). - The second element is the stride to move to the next element within a subarray (which is the same as the stride of the original array). - **Use `np.lib.stride_tricks.as_strided`:** This function creates a new view of the original array `T` with the specified shape and strides. This avoids unnecessary memory copying, making it efficient. 3. **Example Usage:** - An example array `T` is created. - The `strides()` function is called, and the result is printed. **Key Points:** - **Efficiency:** The strided trick is memory-efficient because it creates a new view of the original array without copying the underlying data. - **Flexibility:** You can easily adjust the `window_size` and `step_size` parameters to control the size and overlap of the subarrays. - **Caution:** Be aware that modifying the strided view will modify the original array, as they share the same memory. If you need independent copies, use `np.copy()` on the subarrays.

Extract Code

I extracted the following code from that output:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes a NumPy array and returns a list of overlapping subarrays
  using the strided trick.

  Args:
    T: A 1-dimensional NumPy array of tokens.
    window_size: The desired size of each subarray (default: 50).
    step_size: The stride or step size between the starting indices of 
               consecutive subarrays (default: 10).

  Returns:
    A list of NumPy arrays, where each array is a subarray of T with the 
    specified window_size and overlapping by (window_size - step_size) elements. 
  """

  stride_shape = (len(T) - window_size + 1, window_size)
  strides = (T.strides[0] * step_size, T.strides[0])
  return np.lib.stride_tricks.as_strided(T, stride_shape, strides)

# Example usage
T = np.array(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: True

Run Code Interpreter

Running the following program:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes a NumPy array and returns a list of overlapping subarrays
  using the strided trick.

  Args:
    T: A 1-dimensional NumPy array of tokens.
    window_size: The desired size of each subarray (default: 50).
    step_size: The stride or step size between the starting indices of 
               consecutive subarrays (default: 10).

  Returns:
    A list of NumPy arrays, where each array is a subarray of T with the 
    specified window_size and overlapping by (window_size - step_size) elements. 
  """

  stride_shape = (len(T) - window_size + 1, window_size)
  strides = (T.strides[0] * step_size, T.strides[0])
  return np.lib.stride_tricks.as_strided(T, stride_shape, strides)

# Example usage
T = np.array(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 ...              47
               48              49]
 [             10              11              12 ...              57
               58              59]
 [             20              21              22 ...              67
               68              69]
 ...
 [139739890812848 139739890816048 139739892598256 ... 139739892184944
  139739890916528 139739890924832]
 [139739892184944 139739890916528 139739890917440 ... 139739891961168
  139739890918544 139739891961168]
 [139739892184944 139739892184944 139739890849008 ... 139739892184944
  139739892184816 139739892184944]]
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 35, in <module>
    assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
AssertionError: Wrong answer; got (51, 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

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function uses the strided trick to efficiently extract overlapping windows from an array.

  Args:
    T: A NumPy array of tokens.
    window_size: The desired size of each window.
    step_size: The stride between the start indices of consecutive windows.

  Returns:
    A list of NumPy arrays, where each array represents a window of tokens.
  """

  # Calculate the number of possible windows
  num_windows = (len(T) - window_size) // step_size + 1

  # Use as_strided to create a view of the array with overlapping windows
  # Note: This doesn't copy data, just changes how we view it
  return np.lib.stride_tricks.as_strided(
      T,
      shape=(num_windows, window_size),
      strides=(T.strides[0] * step_size, T.strides[0])
  )

# Example usage:
tokens = np.array(["This", "is", "a", "test", "sentence", "with", "overlapping", "windows", "example"])
windows = strides(tokens, window_size=3, step_size=2)
print(windows)

**Explanation:** 1. **Import numpy:** We need the NumPy library for array manipulation and its `as_strided` function. 2. **Define the function strides(T, window_size=50, step_size=10):** - Takes the input array `T`, desired `window_size`, and `step_size` as arguments. 3. **Calculate the number of windows:** `num_windows = (len(T) - window_size) // step_size + 1` determines how many complete windows fit within the input array. 4. **Use np.lib.stride_tricks.as_strided():** This is the core of the strided trick. - It creates a new view of the array `T` without copying data. - `shape=(num_windows, window_size)` defines the shape of the output (a 2D array where each row is a window). - `strides=(T.strides[0] * step_size, T.strides[0])` is the key part: - `T.strides[0]` is the number of bytes to move to the next element in the original array. - Multiplying it by `step_size` for the first dimension ensures that each row starts `step_size` elements ahead of the previous row. - Using `T.strides[0]` for the second dimension ensures that elements within a row are consecutive elements from the original array. 5. **Return the resulting strided view:** This view represents the overlapping windows. **Key points:** - **Efficiency:** The strided trick avoids unnecessary data copying, making it very memory-efficient, especially for large arrays and windows. - **View, not a copy:** Keep in mind that the returned result is a view of the original data. Modifying elements in the strided view will modify the original array. If you need independent copies, use `.copy()` on the windows. - **Overlapping:** The `step_size` parameter controls the overlap between consecutive windows. A smaller step size results in more overlap.

Extract Code

I extracted the following code from that output:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function uses the strided trick to efficiently extract overlapping windows from an array.

  Args:
    T: A NumPy array of tokens.
    window_size: The desired size of each window.
    step_size: The stride between the start indices of consecutive windows.

  Returns:
    A list of NumPy arrays, where each array represents a window of tokens.
  """

  # Calculate the number of possible windows
  num_windows = (len(T) - window_size) // step_size + 1

  # Use as_strided to create a view of the array with overlapping windows
  # Note: This doesn't copy data, just changes how we view it
  return np.lib.stride_tricks.as_strided(
      T,
      shape=(num_windows, window_size),
      strides=(T.strides[0] * step_size, T.strides[0])
  )

# Example usage:
tokens = np.array(["This", "is", "a", "test", "sentence", "with", "overlapping", "windows", "example"])
windows = strides(tokens, window_size=3, step_size=2)
print(windows)

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:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function uses the strided trick to efficiently extract overlapping windows from an array.

  Args:
    T: A NumPy array of tokens.
    window_size: The desired size of each window.
    step_size: The stride between the start indices of consecutive windows.

  Returns:
    A list of NumPy arrays, where each array represents a window of tokens.
  """

  # Calculate the number of possible windows
  num_windows = (len(T) - window_size) // step_size + 1

  # Use as_strided to create a view of the array with overlapping windows
  # Note: This doesn't copy data, just changes how we view it
  return np.lib.stride_tricks.as_strided(
      T,
      shape=(num_windows, window_size),
      strides=(T.strides[0] * step_size, T.strides[0])
  )

# Example usage:
tokens = np.array(["This", "is", "a", "test", "sentence", "with", "overlapping", "windows", "example"])
windows = strides(tokens, window_size=3, step_size=2)
print(windows)





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:

[['This' 'is' 'a']
 ['a' 'test' 'sentence']
 ['sentence' 'with' 'overlapping']
 ['overlapping' 'windows' 'example']]
All tests passed

Substring Evaluation

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

Final Answer: True

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

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes a numpy array and returns a list of overlapping subarrays 
  using the strided trick.

  Args:
      T (np.ndarray): The input array.
      window_size (int): The size of each subarray.
      step_size (int): The step size between the start of each subarray.

  Returns:
      list: A list of overlapping subarrays.
  """

  # Calculate the number of possible strides
  stride_count = 1 + (len(T) - window_size) // step_size

  # Use as_strided to create a view of the array with overlapping subarrays
  return np.lib.stride_tricks.as_strided(
      T, 
      shape=(stride_count, window_size), 
      strides=(T.strides[0] * step_size, T.strides[0])
  )

# Example usage
T = np.arange(100)
result = strides(T)
print(result)

**Explanation:** 1. **Import numpy:** The code starts by importing the numpy library. 2. **Function Definition:** - The `strides(T, window_size=50, step_size=10)` function takes three arguments: - `T`: The input NumPy array. - `window_size`: The desired size of each subarray (default is 50). - `step_size`: The step size between the starting indices of consecutive subarrays (default is 10). 3. **Calculate stride_count:** This line determines how many complete, overlapping subarrays can be created based on the input array's length, window size, and step size. 4. **Use np.lib.stride_tricks.as_strided:** - This is the core of the strided trick. Instead of copying data to create the subarrays, `as_strided` creates a new view of the original array's data with modified strides. - `shape=(stride_count, window_size)`: This defines the shape of the output array. It will be a 2D array with `stride_count` rows and `window_size` columns. - `strides=(T.strides[0] * step_size, T.strides[0])`: This is the crucial part that defines the overlapping. - `T.strides[0]` gives the number of bytes to move in memory to go from one element to the next in the original array. - Multiplying `T.strides[0]` by `step_size` for the first dimension's stride means that each row in the output array will start `step_size` elements ahead of the previous row, creating the overlap. - The second dimension's stride is simply `T.strides[0]`, meaning elements within a row are consecutive in memory (like a regular subarray). 5. **Return the strided view:** The function returns this new view of the data, which effectively represents the list of overlapping subarrays. **Example Usage:** - An example input array `T` is created using `np.arange(100)`. - The `strides` function is called with the example array, and the result is printed. This approach avoids unnecessary data copying, making it memory-efficient, especially for large arrays and frequent subarray operations.

Extract Code

I extracted the following code from that output:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes a numpy array and returns a list of overlapping subarrays 
  using the strided trick.

  Args:
      T (np.ndarray): The input array.
      window_size (int): The size of each subarray.
      step_size (int): The step size between the start of each subarray.

  Returns:
      list: A list of overlapping subarrays.
  """

  # Calculate the number of possible strides
  stride_count = 1 + (len(T) - window_size) // step_size

  # Use as_strided to create a view of the array with overlapping subarrays
  return np.lib.stride_tricks.as_strided(
      T, 
      shape=(stride_count, window_size), 
      strides=(T.strides[0] * step_size, T.strides[0])
  )

# Example usage
T = np.arange(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: True

Run Code Interpreter

Running the following program:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  This function takes a numpy array and returns a list of overlapping subarrays 
  using the strided trick.

  Args:
      T (np.ndarray): The input array.
      window_size (int): The size of each subarray.
      step_size (int): The step size between the start of each subarray.

  Returns:
      list: A list of overlapping subarrays.
  """

  # Calculate the number of possible strides
  stride_count = 1 + (len(T) - window_size) // step_size

  # Use as_strided to create a view of the array with overlapping subarrays
  return np.lib.stride_tricks.as_strided(
      T, 
      shape=(stride_count, window_size), 
      strides=(T.strides[0] * step_size, T.strides[0])
  )

# Example usage
T = np.arange(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]
 [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 90 91 92 93 94 95 96 97
  98 99]]
All tests passed

Substring Evaluation

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

Final Answer: True

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

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  Extracts overlapping windows from an array using the strided trick.

  Args:
    T: The input array (numpy array).
    window_size: The size of each window.
    step_size: The step size between consecutive windows.

  Returns:
    A list of numpy arrays, each representing a window of the input array.
  """

  stride_shape = (T.size - window_size + step_size) // step_size
  return np.lib.stride_tricks.as_strided(
      T,
      shape=(stride_shape, window_size),
      strides=(T.strides[0] * step_size, T.strides[0]),
  )

# Example usage:
T = np.arange(100)
windows = strides(T)
print(windows)

**Explanation:** 1. **Import numpy:**

   import numpy as np
   

This line imports the NumPy library, which is essential for numerical operations in Python and provides the `as_strided` function for the strided trick. 2. **Define the `strides` function:**

   def strides(T, window_size=50, step_size=10):
     # ... function implementation ...
   

This defines a function named `strides` that takes three arguments: - `T`: The input NumPy array. - `window_size`: The desired size of each window (default is 50). - `step_size`: The step size between the starting indices of consecutive windows (default is 10). 3. **Calculate the number of strides:**

   stride_shape = (T.size - window_size + step_size) // step_size
   

This line determines the number of windows that will be created based on the input array's size, window size, and step size. 4. **Apply the strided trick:**

   return np.lib.stride_tricks.as_strided(
       T,
       shape=(stride_shape, window_size),
       strides=(T.strides[0] * step_size, T.strides[0]),
   )
   

This is the core of the function. It utilizes the `as_strided` function from NumPy to create a new view of the input array `T` without copying the data: - `shape`: Specifies the desired shape of the output array, which is (number of strides, window size). - `strides`: Defines how to move through the original array's memory to create the windows. The first element (`T.strides[0] * step_size`) sets the stride between windows, and the second element (`T.strides[0]`) sets the stride within each window (moving one element at a time). 5. **Example Usage:**

   T = np.arange(100)
   windows = strides(T)
   print(windows)
   

This section provides an example of how to use the `strides` function. It creates a NumPy array `T` with values from 0 to 99, calls the `strides` function to generate the overlapping windows, and then prints the resulting windows. **In summary,** this function efficiently extracts overlapping windows from a NumPy array using the strided trick, avoiding unnecessary data copying and improving performance.

Extract Code

I extracted the following code from that output:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  Extracts overlapping windows from an array using the strided trick.

  Args:
    T: The input array (numpy array).
    window_size: The size of each window.
    step_size: The step size between consecutive windows.

  Returns:
    A list of numpy arrays, each representing a window of the input array.
  """

  stride_shape = (T.size - window_size + step_size) // step_size
  return np.lib.stride_tricks.as_strided(
      T,
      shape=(stride_shape, window_size),
      strides=(T.strides[0] * step_size, T.strides[0]),
  )

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:

import numpy as np

def strides(T, window_size=50, step_size=10):
  """
  Extracts overlapping windows from an array using the strided trick.

  Args:
    T: The input array (numpy array).
    window_size: The size of each window.
    step_size: The step size between consecutive windows.

  Returns:
    A list of numpy arrays, each representing a window of the input array.
  """

  stride_shape = (T.size - window_size + step_size) // step_size
  return np.lib.stride_tricks.as_strided(
      T,
      shape=(stride_shape, window_size),
      strides=(T.strides[0] * step_size, T.strides[0]),
  )





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: All tests passed

Substring Evaluation

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