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Go deh!Mainly Tech projects on Python and Electronic Design Automation.From all truths to (ir)relevancieshttp://paddy3118.blogspot.com/feeds/7018584544614148995/comments/default <div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhI6Tg5CMmhqxo3YLYzw2sarTsTkBTDxuJ8nyTzdwG-C-yFwSB2Z-nu8EJcmRQM0Lw-XcY3obH4uBXrbaf9NXblIdLdcIM4lUfhDU8llOBIzzBdOJuh9TTJOhIaJXDiibJ9Tt8ZNV4ag9ee8Vb7lSs3oqy4w-5R4GAEgJvi3MThZYt-BXgUnWgt/s1024/Gemini_Generated_Image_ahywcpahywcpahyw.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="508" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhI6Tg5CMmhqxo3YLYzw2sarTsTkBTDxuJ8nyTzdwG-C-yFwSB2Z-nu8EJcmRQM0Lw-XcY3obH4uBXrbaf9NXblIdLdcIM4lUfhDU8llOBIzzBdOJuh9TTJOhIaJXDiibJ9Tt8ZNV4ag9ee8Vb7lSs3oqy4w-5R4GAEgJvi3MThZYt-BXgUnWgt/w508-h508/Gemini_Generated_Image_ahywcpahywcpahyw.png" width="508" /></a></div><div _ngcontent-ng-c2711534780="" class="markdown markdown-main-panel enable-updated-hr-color" dir="ltr" id="model-response-message-contentr_fbb1854db7be6292" style="--animation-duration: 400ms; --fade-animation-function: linear; font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px;"><div class="attachment-container generated-images" style="font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px;"><div _ngcontent-ng-c224903204="" class="loader" style="font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px;"> </div></div>Following up on <a href="https://paddy3118.blogspot.com/2025/07/all-truth-in-truthtables.html" rel="nofollow" target="_blank">my previous post</a> about truth tables, I now ask a subtler question: which inputs actually matter? Some variables, though present, leave no trace on the output. In this post, I uncover those quiet bits — the irrelevant inputs — and learn how to spot them with precision.<h4 style="font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px; text-align: left;">Building on the previous</h4>The previous post showed that for a given number of inputs, there is a finite, but rapidly growing, number of possible truth tables. For two inputs, there are 16. For three inputs, there are 256. This leads to a powerful idea: we can create a standardized format for all truth tables and uniquely identify the parts in each one.<h3 style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px;">The Standardized Truth Table (STT) Format</h3><h3 style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px;"> </h3><h3 style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px;"><span style="-webkit-text-stroke-width: 0px; color: #800180; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">i[1] i[0]<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">:<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #274e13; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">r<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">=================<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 0 <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">:<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #04ff00; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">0<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 1 <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">:<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #04ff00; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">0<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">1 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">:<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #04ff00; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">0<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 1 <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">:<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #04ff00; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">1<span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">  </h3>Colour Key<ul style="text-align: left;"><li style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px; text-align: left;">Inputs</li><li style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px; text-align: left;">Input count</li><li style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px; text-align: left;">Result, r </li><li style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px; text-align: left;">Result vector: 0001 </li></ul>We have a standardized truth table with i inputs. Each row in the inputs section of the truth table is an increasing binary count from 0 to 2**i−1. The result column for the truth table has 2**i rows, leading to 2**(2**i) different possible truth table result vectors for i inputs.Any one possible result, r, of the standardized truth table is read as the binary bits of the output going from the topmost row (where the inputs are all zero) down to the bottom-most row, in order. This single binary number, the "result number" or vector, uniquely identifies the truth table and the boolean function it represents.<h3 style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px;">Irrelevant Variables in Boolean Expressions</h3>A variable in a boolean expression is considered irrelevant if its value has no effect on the final output. In a truth table, this means that changing the value of that input variable while all other inputs remain the same does not change the output.While this is easy to understand, it can be difficult to spot for complex functions. This is where the standardized truth table format, STT, comes in handy.<h3 style="font-family: Google Sans, sans-serif; line-height: 1.15; margin-top: 0px;">Irrelevancy Calculation</h3>For an i-input truth table with a 2**2**i bit result, r, we can efficiently check for irrelevant variables. The key insight is that for any single input variable, say i[n], the other input bits change in the exact same order when i[n]=0 as they do when i[n]=1, in the input count region of the STT.Therefore, if the result bits for the rows where i[n]=0 are the same as the result bits for the rows where i[n]=1, then the input variable i[n] has no effect on the output and is therefore irrelevant.Let's illustrate with an example for a 3-input truth table.Example <table><thead><tr><th>i[2]</th> <th>i[1]</th> <th>i[0]</th> <th>:</th> <th align="left">Output</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>0</td> <td>0</td> <td>:</td> <td align="left">r0</td> </tr> <tr> <td>0</td> <td>0</td> <td>1</td> <td>:</td> <td align="left">r1</td> </tr> <tr> <td>0</td> <td>1</td> <td>0</td> <td>:</td> <td align="left">r2</td> </tr> <tr> <td>0</td> <td>1</td> <td>1</td> <td>:</td> <td align="left">r3</td> </tr> <tr> <td>1</td> <td>0</td> <td>0</td> <td>:</td> <td align="left">r4</td> </tr> <tr> <td>1</td> <td>0</td> <td>1</td> <td>:</td> <td align="left">r5</td> </tr> <tr> <td>1</td> <td>1</td> <td>0</td> <td>:</td> <td align="left">r6</td> </tr> <tr> <td>1</td> <td>1</td> <td>1</td> <td>:</td> <td align="left">r7</td></tr></tbody></table> Consider checking if i[1] is irrelevant. The values of the other inputs (i[2] and i[0]) follow the sequence 00, 01, 10, 11 both when i[1] is 0 and when i[1] is 1.<ul style="font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px; padding-inline-start: 32px;"><li style="font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px;">When i[1]=0, the corresponding output bits are r0,r1,r4,r5.</li><li style="font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px;">When i[1]=1, the corresponding output bits are r2,r3,r6,r7.</li></ul>If the sequence of bits (r0,r1,r4,r5) is identical to the sequence of bits (r2,r3,r6,r7), then the input variable i[1] has no effect on the output and is therefore irrelevant.This method allows for a very efficient, algorithmic approach to simplifying boolean expressions. <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>from collections import defaultdict</div><div>import pprint</div> <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"> <div>def is_irrelevant(considered: int,</div><div>                  input_count:int,</div><div>                  result_vector: int) -> bool:</div><div>    """</div><div>    Determine whether a specific input variable is irrelevant to the output of the truth table.</div><div>    </div><div>    Args:</div><div>        considered: Index of the input variable to test.</div><div>        input_count: Total number of input variables.</div><div>        result_vector: Integer representing the output bits of the truth table.</div><div>    </div><div>    Returns:</div><div>        True if the input is irrelevant, False otherwise.</div><div>    """</div><div>    considered_to_resultbits = {0: [], 1: []}</div> <div>    for in_count in range(2**input_count):</div><div>        considered_bit = (in_count >> considered) & 1</div><div>        resultbit = (result_vector >> in_count) & 1</div><div>        considered_to_resultbits[considered_bit].append(resultbit)</div><div>    </div><div>    return considered_to_resultbits[0] == considered_to_resultbits[1]</div></div> <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"> <div>def find_irrelevant_ttable_inputs(input_count:int,</div><div>                                  result_vector: int) -> list[int]:</div><div>    """</div><div>    Identify which input variables are irrelevant in a standardized truth table.</div><div>    </div><div>    Args:</div><div>        input_count: Number of input variables.</div><div>        result_vector: Integer representing the output bits of the truth table.</div><div>    </div><div>    Returns:</div><div>        List of input indices that are irrelevant.</div><div>    """</div><div>    irrelevant = [i for i in range(input_count)</div><div>                  if is_irrelevant(i, input_count, result_vector)]</div><div>    </div><div>    return irrelevant</div> </div></div> <h3 style="font-family: Google Sans Text, sans-serif; line-height: 1.15; margin-top: 0px; text-align: left;">Relevant and irrelevant result vectors for STT's</h3>I am interested in the truthtables/boolean expressions of an increasing number of inputs. Previously I was taking all the zero input STT, then all the 1-input, all the 2-input, ...  That had repetitions and irrelevancies. I can now take just the relevant result vectors for each case, or, take the maximum inputs I can handle and sort the result vectors so that those with the most irrelevancies for that maximum number of inputs, come first.Here's the code I used to investigate these properties of irrelevances: <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>def print_STT_results_sensitive_to_inputs(max_input_count: int) -> None:</div><div>    """</div><div>    Print a summary of irrelevance patterns across all standardized truth tables (STTs)</div><div>    for input counts from 0 up to `max_input_count - 1`.</div> <div>    For each input count `i`, the function:</div><div>      - Computes all possible result vectors (2**(2**i))</div><div>      - Identifies which inputs are irrelevant for each result</div><div>      - Summarizes how many result vectors have at least one irrelevant input</div><div>      - Prints spacing patterns (diffs) between result indices with irrelevance</div><div>      - Checks whether irrelevance indices for input count `i` begin with those from `i-1`</div> <div>    Args:</div><div>        max_input_count: The exclusive upper bound on input counts to analyze.</div><div>    """</div> <div>    imax2irrelevances = {}</div><div>    for imax in range(max_input_count):</div><div>        print(f"\nINPUTS = {imax}\n==========")</div><div>        result_count = 2**2**imax</div><div>        print(f"  total_possible_tt_results =", result_count)</div> <div>        result2irrelevances = {r:[i for i in range(imax)</div><div>                                  if is_irrelevant(i, imax, r)]</div><div>                               for r in range(result_count)}</div><div>        txt = pprint.pformat(result2irrelevances, compact=True, indent=2).replace('\n', '\n  ')</div><div>        txt = txt.replace('{', '{\n   ')</div><div>        #print("  result2irrelevances = ", txt)</div> <div>        irrelevances = [k for k, v in result2irrelevances.items()</div><div>                        if v]</div><div>        </div><div>        imax2irrelevances[imax] = irrelevances</div> <div>        relevances = result_count - len(irrelevances)</div><div>        print(f"  {irrelevances = }")</div><div>        print(f"    {len(irrelevances) = }, {relevances = }")</div> <div>        table = chop_line(format_irrelevance_table_full(result2irrelevances, imax)[0], 220)</div><div>        print("\nSTT Result Irrelevance vs Input Table"</div><div>              f"\n{table}")</div> <div>        # First-order differences between irrelevance indices</div><div>        diff0 = [irrelevances[j] - irrelevances[j-1] </div><div>                 for j in range(1, len(irrelevances))]</div><div>        #print(f"    {diff0 = }")</div> <div>        # Second-order differences between irrelevance indices</div><div>        diff1 = [diff0[j] - diff0[j-1] </div><div>                 for j in range(1, len(diff0))]</div><div>        #print(f"    {diff1 = }")</div><div>    </div><div>    print("\n\nIrrelevance indices reflected about the center of the r count?"</div><div>          f"\n  {is_irrelevance_reflected(imax2irrelevances)}")  # True so far</div> <div>    print('Irrelevances for `i` inputs begin with those from `i-1`?')</div><div>    previous_prefixed = all((i0 := imax2irrelevances[i-1]) == imax2irrelevances[i][:len(i0)]</div><div>                            for i in range(1, max_input_count))</div><div>    print(f"  {previous_prefixed}")  # True so far</div> <div>def is_irrelevance_reflected(ins2irrel: dict[int, list[int]]) -> bool:</div><div>    """</div><div>    Check whether irrelevance indices are symmetric about the center of the result vector space.</div> <div>    For each input count `i`, the result vector space has size 2**(2**i).</div><div>    This function checks whether for every irrelevance index `r < half_range`,</div><div>    its mirror index `full_range - 1 - r` is also marked as irrelevant.</div> <div>    Args:</div><div>        ins2irrel: Mapping from input count to list of result indices with irrelevance.</div> <div>    Returns:</div><div>        True if all irrelevance sets are symmetric about the center, False otherwise.</div><div>    """</div><div>    for imax, irrelevances in ins2irrel.items():</div><div>        irrel_set = set(irrelevances)</div><div>        full_range = 2**2**imax</div><div>        half_range = full_range / 2</div> <div>        if not all((full_range - 1 - i) in irrel_set for i in irrel_set if i < half_range):</div><div>            return False</div><div>    return True</div> <div>def format_irrelevance_table(result2irrelevances: dict[int, list[int]], input_count: int) -> tuple[str, str]:</div><div>    """</div><div>    Create a compact table showing which inputs are irrelevant for each result vector `r`.</div> <div>    Each column is sized to match the width of its corresponding `r` value.</div><div>    Only includes columns for `r` values that have at least one irrelevant input.</div><div>    Each row corresponds to an input index, with '@' if that input is irrelevant for that `r`, else blank.</div> <div>    Returns:</div><div>        A tuple of (plain text table string, markdown table string)</div><div>    """</div><div>    if input_count == 0:</div><div>        msg = "No inputs to analyze (input_count = 0)."</div><div>        md = "| Input | (none) |\n|-------|--------|\n| (none) |        |"</div><div>        return msg, md</div> <div>    filtered_r = [r for r, irrels in result2irrelevances.items() if irrels]</div><div>    if not filtered_r:</div><div>        msg = "No irrelevant inputs found for any result vector."</div><div>        md = "| Input | (none) |\n|-------|--------|\n" + "\n".join(</div><div>            f"| i[{i}] |        |" for i in range(input_count)</div><div>        )</div><div>        return msg, md</div> <div>    # Determine individual column widths based on r string length</div><div>    r_labels = [str(r) for r in filtered_r]</div><div>    col_widths = [len(label) for label in r_labels]</div> <div>    # Header row</div><div>    header_plain = " " * 8 + " ".join(label.rjust(w) for label, w in zip(r_labels, col_widths))</div><div>    header_md = "| Input | " + " | ".join(label.rjust(w) for label, w in zip(r_labels, col_widths)) + " |"</div> <div>    # Markdown separator row</div><div>    separator_md = "|-------|" + "|".join("-" * (w + 2) for w in col_widths) + "|"</div> <div>    # Rows</div><div>    rows_plain = []</div><div>    rows_md = []</div><div>    for i in range(input_count):</div><div>        label = f"i[{i}]".ljust(8)</div><div>        row_plain = label + " ".join("@" .rjust(w) if i in result2irrelevances[r] else " " * w</div><div>                                     for r, w in zip(filtered_r, col_widths))</div><div>        row_md = "| " + f"i[{i}]".ljust(5) + " | " + " | ".join("@" .rjust(w) if i in result2irrelevances[r] else " " * w</div><div>                                                               for r, w in zip(filtered_r, col_widths)) + " |"</div><div>        rows_plain.append(row_plain)</div><div>        rows_md.append(row_md)</div> <div>    plain_table = "\n".join([header_plain] + rows_plain)</div><div>    markdown_table = "\n".join([header_md, separator_md] + rows_md)</div> <div>    return plain_table, markdown_table</div> <div>def format_irrelevance_table_full(result2irrelevances: dict[int, list[int]], input_count: int) -> tuple[str, str]:</div><div>    """</div><div>    Create a full table showing which inputs are irrelevant for each result vector `r`.</div> <div>    Includes columns for all result vector indices (0 to max(r)).</div><div>    Each row corresponds to an input index, with '@' if that input is irrelevant for that `r`, else blank.</div> <div>    Returns:</div><div>        A tuple of (plain text table string, markdown table string)</div><div>    """</div><div>    if input_count == 0:</div><div>        msg = "No inputs to analyze (input_count = 0)."</div><div>        md = "| Input | (none) |\n|-------|--------|\n| (none) |        |"</div><div>        return msg, md</div> <div>    all_r = sorted(result2irrelevances.keys())</div><div>    r_labels = [str(r) for r in all_r]</div><div>    col_widths = [len(label) for label in r_labels]</div> <div>    # Header row</div><div>    header_plain = " " * 8 + " ".join(label.rjust(w) for label, w in zip(r_labels, col_widths))</div><div>    header_md = "| Input | " + " | ".join(label.rjust(w) for label, w in zip(r_labels, col_widths)) + " |"</div> <div>    # Markdown separator row</div><div>    separator_md = "|-------|" + "|".join("-" * (w + 2) for w in col_widths) + "|"</div> <div>    # Rows</div><div>    rows_plain = []</div><div>    rows_md = []</div><div>    for i in range(input_count):</div><div>        label = f"i[{i}]".ljust(8)</div><div>        row_plain = label + " ".join("@" .rjust(w) if i in result2irrelevances.get(r, []) else " " * w</div><div>                                     for r, w in zip(all_r, col_widths))</div><div>        row_md = "| " + f"i[{i}]".ljust(5) + " | " + " | ".join("@" .rjust(w) if i in result2irrelevances.get(r, []) else " " * w</div><div>                                                               for r, w in zip(all_r, col_widths)) + " |"</div><div>        rows_plain.append(row_plain)</div><div>        rows_md.append(row_md)</div> <div>    plain_table = "\n".join([header_plain] + rows_plain)</div><div>    markdown_table = "\n".join([header_md, separator_md] + rows_md)</div> <div>    return plain_table, markdown_table</div> <div>def chop_line(table_text: str, max_length: int) -> str:</div><div>    """</div><div>    Chop each line in a multi-line string to a maximum length.</div><div>    If a line exceeds `max_length`, it is truncated to `max_length - 3` and '...' is appended.</div> <div>    Args:</div><div>        table_text: The full multi-line string to process.</div><div>        max_length: The maximum allowed line length.</div> <div>    Returns:</div><div>        A new multi-line string with long lines chopped and marked with '...'.</div><div>    """</div><div>    chopped_lines = []</div><div>    for line in table_text.splitlines():</div><div>        if len(line) > max_length:</div><div>            chopped_lines.append(line[:max_length - 3] + '...')</div><div>        else:</div><div>            chopped_lines.append(line)</div><div>    return '\n'.join(chopped_lines)</div> <div>print_STT_results_sensitive_to_inputs(5)</div><div> </div></div> </div><h3 style="text-align: left;">Irrelevances: Output</h3> <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>INPUTS = 0</div><div>==========</div><div>  total_possible_tt_results = 2</div><div>  irrelevances = []</div><div>    len(irrelevances) = 0, relevances = 2</div> <div>STT Result Irrelevance vs Input Table</div><div>No inputs to analyze (input_count = 0).</div> <div>INPUTS = 1</div><div>==========</div><div>  total_possible_tt_results = 4</div><div>  irrelevances = [0, 3]</div><div>    len(irrelevances) = 2, relevances = 2</div> <div>STT Result Irrelevance vs Input Table</div><div>        0 1 2 3</div><div>i[0]    @     @</div> <div>INPUTS = 2</div><div>==========</div><div>  total_possible_tt_results = 16</div><div>  irrelevances = [0, 3, 5, 10, 12, 15]</div><div>    len(irrelevances) = 6, relevances = 10</div> <div>STT Result Irrelevance vs Input Table</div><div>        0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15</div><div>i[0]    @     @                    @        @</div><div>i[1]    @         @          @              @</div> <div>INPUTS = 3</div><div>==========</div><div>  total_possible_tt_results = 256</div><div>  irrelevances = [0, 3, 5, 10, 12, 15, 17, 34, 48, 51, 60, 63, 68, 80, 85, 90, 95, 102, 119, 136, 153, 160, 165, 170, 175, 187, 192, 195, 204, 207, 221, 238, 240, 243, 245, 250, 252, 255]</div><div>    len(irrelevances) = 38, relevances = 218</div> <div>STT Result Irrelevance vs Input Table</div><div>        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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 ...</div><div>i[0]    @     @                    @        @                                                                                                  @        @                          @        @                            ...</div><div>i[1]    @         @          @              @                                                                                                                                                                            ...</div><div>i[2]    @                                         @                                                  @                                                  @                                                  @             ...</div> <div>INPUTS = 4</div><div>==========</div><div>  total_possible_tt_results = 65536</div><div>  irrelevances = [0, 3, 5, 10, 12, 15, 17, 34, 48, 51, 60, 63, 68, 80, 85, 90, 95, 102, 119, 136, 153, 160, 165, 170, 175, 187, 192, 195, 204, 207, 221, 238, 240, 243, 245, 250, 252, 255, 257, 514, 768, 771, 780, 783, 816, 819, 828, 831, 960, 963, 972, 975, 1008, 1011, 1020, 1023, 1028, 1280, 1285, 1290, 1295, 1360, 1365, 1370, 1375, 1440, 1445, 1450, 1455, 1520, 1525, 1530, 1535, 1542, 1799, 2056, 2313, 2560, 2565, 2570, 2575, 2640, 2645, 2650, 2655, 2720, 2725, 2730, 2735, 2800, 2805, 2810, 2815, 2827, 3072, 3075, 3084, 3087, 3120, 3123, 3132, 3135, 3264, 3267, 3276, 3279, 3312, 3315, 3324, 3327, 3341, 3598, 3840, 3843, 3845, 3850, 3852, 3855, 3888, 3891, 3900, 3903, 3920, 3925, 3930, 3935, 4000, 4005, 4010, 4015, 4032, 4035, 4044, 4047, 4080, 4083, 4085, 4090, 4092, 4095, 4112, 4352, 4369, 4386, 4403, 4420, 4437, 4454, 4471, 4488, 4505, 4522, 4539, 4556, 4573, 4590, 4607, 4626, 4883, 5140, 5397, 5654, 5911, 6168, 6425, 6682, 6939, 7196, 7453, 7710, 7967, 8224, 8481, 8704, 8721, 8738, 8755, 8772, 8789, 8806, 8823, 8840, 8857, 8874, 8891, 8908, 8925, 8942, 8959, 8995, 9252, 9509, 9766, 10023, 10280, 10537, 10794, 11051, 11308, 11565, 11822, 12079, 12288, 12291, 12300, 12303, 12336, 12339, 12348, 12351, 12480, 12483, 12492, 12495, 12528, 12531, 12540, 12543, 12593, 12850, 13056, 13059, 13068, 13071, 13073, 13090, 13104, 13107, 13116, 13119, 13124, 13141, 13158, 13175, 13192, 13209, 13226, 13243, 13248, 13251, 13260, 13263, 13277, 13294, 13296, 13299, 13308, 13311, 13364, 13621, 13878, 14135, 14392, 14649, 14906, 15163, 15360, 15363, 15372, 15375, 15408, 15411, 15420, 15423, 15552, 15555, 15564, 15567, 15600, 15603, 15612, 15615, 15677, 15934, 16128, 16131, 16140, 16143, 16176, 16179, 16188, 16191, 16320, 16323, 16332, 16335, 16368, 16371, 16380, 16383, 16448, 16705, 16962, 17219, 17408, 17425, 17442, 17459, 17476, 17493, 17510, 17527, 17544, 17561, 17578, 17595, 17612, 17629, 17646, 17663, 17733, 17990, 18247, 18504, 18761, 19018, 19275, 19532, 19789, 20046, 20303, 20480, 20485, 20490, 20495, 20560, 20565, 20570, 20575, 20640, 20645, 20650, 20655, 20720, 20725, 20730, 20735, 20817, 21074, 21331, 21588, 21760, 21765, 21770, 21775, 21777, 21794, 21811, 21828, 21840, 21845, 21850, 21855, 21862, 21879, 21896, 21913, 21920, 21925, 21930, 21935, 21947, 21964, 21981, 21998, 22000, 22005, 22010, 22015, 22102, 22359, 22616, 22873, 23040, 23045, 23050, 23055, 23120, 23125, 23130, 23135, 23200, 23205, 23210, 23215, 23280, 23285, 23290, 23295, 23387, 23644, 23901, 24158, 24320, 24325, 24330, 24335, 24400, 24405, 24410, 24415, 24480, 24485, 24490, 24495, 24560, 24565, 24570, 24575, 24672, 24929, 25186, 25443, 25700, 25957, 26112, 26129, 26146, 26163, 26180, 26197, 26214, 26231, 26248, 26265, 26282, 26299, 26316, 26333, 26350, 26367, 26471, 26728, 26985, 27242, 27499, 27756, 28013, 28270, 28527, 28784, 29041, 29298, 29555, 29812, 30069, 30326, 30464, 30481, 30498, 30515, 30532, 30549, 30566, 30583, 30600, 30617, 30634, 30651, 30668, 30685, 30702, 30719, 30840, 31097, 31354, 31611, 31868, 32125, 32382, 32639, 32896, 33153, 33410, 33667, 33924, 34181, 34438, 34695, 34816, 34833, 34850, 34867, 34884, 34901, 34918, 34935, 34952, 34969, 34986, 35003, 35020, 35037, 35054, 35071, 35209, 35466, 35723, 35980, 36237, 36494, 36751, 37008, 37265, 37522, 37779, 38036, 38293, 38550, 38807, 39064, 39168, 39185, 39202, 39219, 39236, 39253, 39270, 39287, 39304, 39321, 39338, 39355, 39372, 39389, 39406, 39423, 39578, 39835, 40092, 40349, 40606, 40863, 40960, 40965, 40970, 40975, 41040, 41045, 41050, 41055, 41120, 41125, 41130, 41135, 41200, 41205, 41210, 41215, 41377, 41634, 41891, 42148, 42240, 42245, 42250, 42255, 42320, 42325, 42330, 42335, 42400, 42405, 42410, 42415, 42480, 42485, 42490, 42495, 42662, 42919, 43176, 43433, 43520, 43525, 43530, 43535, 43537, 43554, 43571, 43588, 43600, 43605, 43610, 43615, 43622, 43639, 43656, 43673, 43680, 43685, 43690, 43695, 43707, 43724, 43741, 43758, 43760, 43765, 43770, 43775, 43947, 44204, 44461, 44718, 44800, 44805, 44810, 44815, 44880, 44885, 44890, 44895, 44960, 44965, 44970, 44975, 45040, 45045, 45050, 45055, 45232, 45489, 45746, 46003, 46260, 46517, 46774, 47031, 47288, 47545, 47802, 47872, 47889, 47906, 47923, 47940, 47957, 47974, 47991, 48008, 48025, 48042, 48059, 48076, 48093, 48110, 48127, 48316, 48573, 48830, 49087, 49152, 49155, 49164, 49167, 49200, 49203, 49212, 49215, 49344, 49347, 49356, 49359, 49392, 49395, 49404, 49407, 49601, 49858, 49920, 49923, 49932, 49935, 49968, 49971, 49980, 49983, 50112, 50115, 50124, 50127, 50160, 50163, 50172, 50175, 50372, 50629, 50886, 51143, 51400, 51657, 51914, 52171, 52224, 52227, 52236, 52239, 52241, 52258, 52272, 52275, 52284, 52287, 52292, 52309, 52326, 52343, 52360, 52377, 52394, 52411, 52416, 52419, 52428, 52431, 52445, 52462, 52464, 52467, 52476, 52479, 52685, 52942, 52992, 52995, 53004, 53007, 53040, 53043, 53052, 53055, 53184, 53187, 53196, 53199, 53232, 53235, 53244, 53247, 53456, 53713, 53970, 54227, 54484, 54741, 54998, 55255, 55512, 55769, 56026, 56283, 56540, 56576, 56593, 56610, 56627, 56644, 56661, 56678, 56695, 56712, 56729, 56746, 56763, 56780, 56797, 56814, 56831, 57054, 57311, 57568, 57825, 58082, 58339, 58596, 58853, 59110, 59367, 59624, 59881, 60138, 60395, 60652, 60909, 60928, 60945, 60962, 60979, 60996, 61013, 61030, 61047, 61064, 61081, 61098, 61115, 61132, 61149, 61166, 61183, 61423, 61440, 61443, 61445, 61450, 61452, 61455, 61488, 61491, 61500, 61503, 61520, 61525, 61530, 61535, 61600, 61605, 61610, 61615, 61632, 61635, 61644, 61647, 61680, 61683, 61685, 61690, 61692, 61695, 61937, 62194, 62208, 62211, 62220, 62223, 62256, 62259, 62268, 62271, 62400, 62403, 62412, 62415, 62448, 62451, 62460, 62463, 62708, 62720, 62725, 62730, 62735, 62800, 62805, 62810, 62815, 62880, 62885, 62890, 62895, 62960, 62965, 62970, 62975, 63222, 63479, 63736, 63993, 64000, 64005, 64010, 64015, 64080, 64085, 64090, 64095, 64160, 64165, 64170, 64175, 64240, 64245, 64250, 64255, 64507, 64512, 64515, 64524, 64527, 64560, 64563, 64572, 64575, 64704, 64707, 64716, 64719, 64752, 64755, 64764, 64767, 65021, 65278, 65280, 65283, 65285, 65290, 65292, 65295, 65297, 65314, 65328, 65331, 65340, 65343, 65348, 65360, 65365, 65370, 65375, 65382, 65399, 65416, 65433, 65440, 65445, 65450, 65455, 65467, 65472, 65475, 65484, 65487, 65501, 65518, 65520, 65523, 65525, 65530, 65532, 65535]</div><div>    len(irrelevances) = 942, relevances = 64594</div> <div>STT Result Irrelevance vs Input Table</div><div>        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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 ...</div><div>i[0]    @     @                    @        @                                                                                                  @        @                          @        @                            ...</div><div>i[1]    @         @          @              @                                                                                                                                                                            ...</div><div>i[2]    @                                         @                                                  @                                                  @                                                  @             ...</div><div>i[3]    @                                                                                                                                                                                                                ...</div> <div>Irrelevance indices reflected about the center of the r count?</div><div>  True</div><div>Irrelevances for `i` inputs begin with those from `i-1`?</div><div>  True</div> </div><h3 style="text-align: left;">OEIS</h3>The sequence of relevances: 2, 2, 10, 218, 64594 is already present as <a href="https://oeis.org/A000371" rel="nofollow" target="_blank">A000371 </a>on OEIS.The sequences of irelevances: 0, 2, 6, 38, 942 had no exact match although <a href="https://oeis.org/search?q=2%2c6%2c38%2c942%20id:A005530" target="_blank">A005530 </a>came close END. Sun, 14 Sep 2025 12:51:00 Go Deh celebrations!http://paddy3118.blogspot.com/feeds/3066718111574923076/comments/default <div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEghCYUXmcR6-kfrZKbVof_gfZfqHnOE_cfxQx9l6Iii9pjsuFXpRuudMkEU5_gjPHNfsa34AFbNCefCkvGwfEOaCKGqXGzQfTZTsdoG9YUXkj9oGDfRoozESPySqOszV5PquQUtHDHG2-2snTbL3TAdo1FLEfeemLpuxJXHU7NFbUPKpagEmM48/s1024/A%20jubilant%20Jamaican%202.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEghCYUXmcR6-kfrZKbVof_gfZfqHnOE_cfxQx9l6Iii9pjsuFXpRuudMkEU5_gjPHNfsa34AFbNCefCkvGwfEOaCKGqXGzQfTZTsdoG9YUXkj9oGDfRoozESPySqOszV5PquQUtHDHG2-2snTbL3TAdo1FLEfeemLpuxJXHU7NFbUPKpagEmM48/s320/A%20jubilant%20Jamaican%202.png" width="320" /></a></div> <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_z_l56oVliXpceL0jsn8lbb_wjVN9KF7c4vD0IAzKv76gWTvREQCqDvXJQa-Y7UlxdTCUbSYja7bSwPmRvf5yTo_E4sJW61CZTNdC2-utWn28tI0RuEPhRCbQqfb73_LddRGry9Sa7bNhAp6eDx8x4clBJAkm3__Tjr39byNDqiRlNdJFkQAi/s2048/Gemini_Generated_Image_pvdkv4pvdkv4pvdk.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2048" data-original-width="2048" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_z_l56oVliXpceL0jsn8lbb_wjVN9KF7c4vD0IAzKv76gWTvREQCqDvXJQa-Y7UlxdTCUbSYja7bSwPmRvf5yTo_E4sJW61CZTNdC2-utWn28tI0RuEPhRCbQqfb73_LddRGry9Sa7bNhAp6eDx8x4clBJAkm3__Tjr39byNDqiRlNdJFkQAi/s320/Gemini_Generated_Image_pvdkv4pvdkv4pvdk.png" width="320" /></a></div>  This year it's: <ol style="text-align: left;"><li>Fifty years since I first learned to program!</li><li>Thirty years of programming Python!</li><li>And In July, this blog topped one million views since its inception! </li></ol>I first learned to program by crashing an after-school class I saw the private school geeks disappearing to from Central library  in Nottingham. One of the Trent Polytechnic lecturers I now see sometimes on Computerphile - he mentioned how the council payed something towards the cost of the computer on the understanding that they have a class for students, but somehow only the guys from the fee paying Nottingham High School for Boys seemed to get the memo, hmm. That was s long ago, Paper tape and teletypes.Forward to 1995: A new job, amongst engineers where the few of them that could program, programmed in Perl. Perl was big within the chip design industry back then, but Aargh! I thought Perl was awful - for me it did not gel! I went looking for an alternative and found this little known language called Python.  The community on comp.lang.python seemed so nice, they valued readability and "One obvious way to do it" over Perls more than one way. I was hooked, and did my little bit of evangelizing. (OK, I was known as that Python guy - but they couldn't fault the code).Was it eleven years ago that I started the blog? It has been one of two main places where I have shown my work - the other is on site<a href="https://rosettacode.org/wiki/User:Paddy3118" rel="nofollow" target="_blank"> rosettacode.org</a> which, no doubt, has been scraped to death for AI's - it is one of the few places online showing different programming languages solving the same task. I wrote 199 of those RosettaCode tasks over the years, and many more of the examples solving those tasks; (mainly  Python and Awk language examples). Most, if not all of those task descriptions were my own work - not copied off other sites - if there are similarities in text then they probably copied from me or the task is so simple that there is going to be convergence). Lately, as you can tell from the pictures above, I have been dabbling in AI. AI is the future, but who knows how AI will affect that future? I am a programmer who can describe coding tasks to other people for them to complete the task. AI is an ever changing field, I think that I am learning to use it better, as it gets better too - I am both more and less precise in my prompting and finding out what the AI remembers and does best. I experiment with the  amount of description in the code to cut and paste into new sessions to cut the mistakes and continue from a clean base. I experiment with coding from pseudo-code. I have found that some abilities are universal: when exploring the use of SQL - which I am a hunt-n-peck coder in, I can still spot redundancies "You recompute the first query as part of the second and the second as part of the third, can't you reuse prior results", lead to the AI introducing common table expressions I think it was, and telling me all about them.I'm still scripting  😊 Sun, 10 Aug 2025 17:22:00 All Truth in Truthtables!http://paddy3118.blogspot.com/feeds/1386948987337358897/comments/default  <img alt="" height="236" src="https://blogger.googleusercontent.com/img/a/AVvXsEjAkKWov5Vkf0RC3lLoj7mdzZPEW2RRK5fqNDoD1wzaRXh8UUXI9pd3gYbneEhapx8xw2MprxeoN3Zmb1E-5scChoMYLZgcygDR8cwG7BaQtEZkWeYlMn8RUPWTWX4jerFY2kU0NR_AkSUfjVx85ifTtOIhzJq1ZQT_IXVIqtqJ3qBATtr2l0w4=w236-h236" width="236" />  (Best viewed on a larger than phone screen)To crib from my <a href="https://rosettacode.org/wiki/Truth_table" rel="nofollow" target="_blank">RosettaCode tasks</a> description and examples:<blockquote>A <a class="extiw" href="https://en.wikipedia.org/wiki/Truth_table" title="wp:Truth table">truth table</a> is a display of the inputs to, and the output of a Boolean equation organised as a table where each row gives one combination of input values and the corresponding value of the equation.</blockquote>And as examples:<pre><blockquote>Boolean expression: A ^ B A B : A ^ B 0 0 : 0 0 1 : 1 1 0 : 1 1 1 : 0 Boolean expression: S | ( T ^ U ) S T U : S | ( T ^ U ) 0 0 0 : 0 0 0 1 : 1 0 1 0 : 1 0 1 1 : 0 1 0 0 : 1 1 0 1 : 1 1 1 0 : 1 1 1 1 : 1</blockquote></pre><h2 style="text-align: left;"> Format</h2>A truth table has a header row of columns showing first the names of inputs assigned to each column; a visual separator - e.g. ':'; then the column name for the output result.The body of the table, under the inputs section, contains rows of all binary combinations of the inputs. It is usually arranged as each row of the input section being a binary count from zero to 2**input_count - 1  The body of the table, under the result section, contains rows showing the binary output produced from the input configuration in the same row, to the left.<h3 style="text-align: left;">Format used</h3> I am interested in the number of inputs rather than their names so will show vector i with the most significant indices to the left, (so the binary count in the input sections body looks right).Similarly I am interested in the bits in the result column rather than a name so will just call the result column r.<h2 style="text-align: left;">From one result to many</h2> Here's the invocation, and truth tables produced for some simple boolean operators:<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>print("\nOR\n")</div><div>pp_ttable(2, 0b1110)</div></div> <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">OR <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">i[1] i[0] : r <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">================= <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 0 : 0 <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 1 : 1 <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 0 : 1 <span style="-webkit-text-stroke-width: 0px; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 1 : 1 <span style="-webkit-text-stroke-width: 0px; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; widows: 2; word-spacing: 0px;"> <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>print("\nXOR\n")</div><div>pp_ttable(2, 0b110)</div></div><span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">XOR <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">i[1] i[0] : r <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">================= <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 0 : 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 1 : 1 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 0 : 1 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 1 : 0 <span style="-webkit-text-stroke-width: 0px; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; widows: 2; word-spacing: 0px;"> <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>print("\nAND\n")</div><div>pp_ttable(2, 0b1000)</div></div><span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">AND <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">i[1] i[0] : r <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">================= <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 0 : 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 1 : 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 0 : 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 1 : 1 For those three inputs, we can extend the table to show result columns for OR, XOR and then AND, like this:<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>print("\nOR, XOR, then AND result *columns*\n")</div><div>pp_ttable(2, [14, 6, 8])  # A list of results, (in decimal this time)</div></div><span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">OR, XOR, then AND result *columns* <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">i[1] i[0] : r[0] r[1] r[2] <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">=========================== <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 0 : 0 0 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 1 : 1 1 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 0 : 1 1 0 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 1 : 1 0 1 <h2 style="text-align: left;">All Truth</h2> Just how many results are possible?Well, i = 2 inputs gives 2**i = 4 possible input boolean combinations; so a result column has 2**i = 4  bits. The number of different result columns is therefore 2**(2**i) = 2**4 = 16We can show all possible results by successive results being a binary count, but this time by column in the results section, (with the LSB being closest to the header row)The pp_table function automatically  generates all possible results if a second parameter of None is used<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>print("\nAll Truths of two inputs!\n")</div><div>pp_ttable(2, None)</div></div><span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">All Truths of two inputs! <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">i[1] i[0] : r[0] r[1] r[2] r[3] r[4] r[5] r[6] r[7] r[8] r[9] r[10] r[11] r[12] r[13] r[14] r[15] <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">============================================================================================================== <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 0 : 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 0 1 : 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 0 : 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 <span style="-webkit-text-stroke-width: 0px; color: #2b00fe; font-family: Consolas, "Courier New", monospace; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;"> 1 1 : 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1  We might say that it shows all possible truths for up-to-and-including two inputs. That is because results include outputs not dependent on any or all of those two inputs. For example r[0], and r[15] do not depend on any input as they give constant outputs of 0 and 1, respectively. r[5] is simply ~i[0] and does not depend on i[1]<h3 style="text-align: left;">It's Big, Oh!</h3>The results grow as  2**(2**i), sometimes called double exponential growth! it gets large, quickly!! <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">i | 2**(2**i) <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">--|---------- <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">0 | 2 <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">1 | 4 <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">2 | 16 <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">3 | 256 <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">4 | 65_536 <span style="-webkit-text-stroke-width: 0px; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; word-spacing: 0px;">5 | 4_294_967_296<span style="-webkit-text-stroke-width: 0px; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: pre; widows: 2; word-spacing: 0px;"> <h1 style="text-align: left;">The code</h1> Contemplating using AI and needing to get it to understand what I wanted, as well as endless prompting to get it to do what I want, the way I wanted it; I decided on writing it all by myself - I knew I wanted it just so, and the coding would be nothing new to me, just nailing what I wanted to show. <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>"""</div><div>Truthtable prettyprinter</div> <div>Author: Paddy3118  2025/07/19</div> <div># Regions of a truthtable</div> <div>```text</div><div>+----------------- --+---------+</div><div>|      Inputs        | Result  |</div><div>+------------------- +---------+</div><div>| I[2] | I[1] | I[0] |   R     |</div><div>|------|------|------|---------|</div><div>|  0   |  0   |  0   |   0     |</div><div>|  0   |  0   |  1   |   1     |</div><div>|  0   |  1   |  0   |   0     |</div><div>|  0   |  1   |  1   |   1     |</div><div>|  1   |  0   |  0   |   1     |</div><div>|  1   |  0   |  1   |   0     |</div><div>|  1   |  1   |  0   |   1     |</div><div>|  1   |  1   |  1   |   0     |</div><div>+--------------------+---------+</div><div>```</div> <div>"""</div> <div># %%</div><div>def pp_ttable(input_count:int,</div><div>              result_vector: int | list[int] | None,</div><div>              input_vector_name: str='i',</div><div>              result_vector_name: str='r'</div><div>              ) -> None:</div><div>    r_single = r_mult = False</div><div>    match result_vector:</div><div>        case int():     # single result</div><div>            r_single = True</div><div>            r_width = len(f" {result_vector_name}")</div><div>            result_vector = [result_vector]</div><div>        case list():    # multiple results</div><div>            r_mult = True</div><div>            r_width = len(f" {result_vector_name}[{len(result_vector) - 1}]")</div><div>        case None:      # All possible results</div><div>            assert input_count < 4, f"Refuse to write {2**(2**input_count)} result columns."</div><div>            r_mult = True</div><div>            result_vector = list(range(2**(2**input_count)))</div><div>            r_width = len(f" {result_vector_name}[{len(result_vector) - 1}]")</div><div>        case _:</div><div>            raise Typeerror("result must be an int, a list of ints, or None.")</div> <div>    col_width = max(len(f" {input_vector_name}[{input_count - 1}]"), r_width)</div> <div>    rows = []</div><div>    header = []</div><div>    for i in range(input_count-1, -1, -1):</div><div>        col = f"{input_vector_name}[{i:}]"</div><div>        header.append(f"{col:{col_width}}")</div><div>    header.append(': ')</div><div>    if r_single:</div><div>        header.append(f"{result_vector_name:^{col_width}}")</div><div>    else:</div><div>        for i in range(len(result_vector)):</div><div>            col = f"{result_vector_name}[{i:}]"</div><div>            header.append(f"{col:^{col_width}}")</div><div>    rows.append(''.join(header))</div><div>    rows.append('=' * len(rows[-1]))    # header row spacer</div> <div>    result_bits = [f"{res:0{2**input_count}b}"[::-1]</div><div>                    for res in result_vector]</div><div>    </div><div>    for r in range(2**input_count):</div><div>        row = []</div><div>        col_bits = f"{r:0{input_count}b}"</div><div>        for bit in col_bits:</div><div>            row.append(f"{bit:^{col_width}}")</div><div>        </div><div>        row.append(': ')</div> <div>        for res_bits in result_bits:</div><div>            row.append(f"{res_bits[r]:^{col_width}}")</div><div>                </div><div>        rows.append(''.join(row))</div> <div>    print('\n'.join(rows))</div> <div># %%</div><div>print("\nOR\n")</div><div>pp_ttable(2, 0b1110)</div><div>print("\nXOR\n")</div><div>pp_ttable(2, 0b110)</div><div>print("\nAND\n")</div><div>pp_ttable(2, 0b1000)</div><div>    </div> <div># %%</div> <div>print("\nOR, XOR, then AND result *columns*\n")</div><div>pp_ttable(2, [14, 6, 8])  # A list of results, (in decimal this time)</div> <div># %%</div><div>print("\nAll Truths of two inputs!\n")</div><div>pp_ttable(2, None)</div> <div># %%</div> </div> <h4 style="text-align: left;">END. </h4>     Sun, 20 Jul 2025 10:28:00 Incremental combinations without cachinghttp://paddy3118.blogspot.com/feeds/6157910081675774828/comments/default <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiDhcFwRluMUmESYsqTtkwNppqf1z4krD48fhecEBVWqwxG6UN6vaA1CctLiNef3T3kEUip9mQsHGaK6zfHGNo-PICWgj2vo9mFdMU-xSGImhgl8PRc3wgI172uD1R1zRtYCja71vCLWxIZvitL-xTN0cFNHb8jTdTL-JqBYlp50D4CjJuJRuz2" style="margin-left: auto; margin-right: auto;"><img alt="" data-original-height="1024" data-original-width="1024" height="534" src="https://blogger.googleusercontent.com/img/a/AVvXsEiDhcFwRluMUmESYsqTtkwNppqf1z4krD48fhecEBVWqwxG6UN6vaA1CctLiNef3T3kEUip9mQsHGaK6zfHGNo-PICWgj2vo9mFdMU-xSGImhgl8PRc3wgI172uD1R1zRtYCja71vCLWxIZvitL-xTN0cFNHb8jTdTL-JqBYlp50D4CjJuJRuz2=w534-h534" width="534" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Irie server room</td></tr></tbody></table> Someone had <a href="https://discuss.python.org/t/incremental-combination-generation-from-laggy-generators/85052/2" rel="nofollow" target="_blank">a problem</a> where they received initial data d1, worked on all r combinations of the data initially received, but by the time they had finished that, they checked and found there was now extra data d2, and they need to, in total, process the r combinations of all data d1+d2.They don't want to process combinations twice, and the solutions given seemed to generate and store the combinations of  d1, then generate the combinations of d1+d2 but test and reject any combination that was found previously.Seems like a straight-forward answer that is easy to follow, but I thought: Is there a way to create just the extra combinations but without storing all the combinations from before?<h2 style="text-align: left;">My methods</h2>It's out with Vscode as my IDE. I'll be playing with a lot of code that will end up deleted, modified, rerun. I could use a Jupyter notebook, but I can't publish them to my blog satisfactorily. I'll develop a .py file but with cells: a line comment of # %% visually splits the file into cells in the IDE adding buttons and editor commands to execute cells and selected code, in any order, on a restartable kernel running in an interactive window that also runs Ipython.When doodling like this, I often create long lines, spaced to highlight comparisons between other lines. I refactor names to be concise at the time, as what you can take in at a glance helps find patterns. Because of that, this blog post is not written to be read on the small screens of phones. So, its combinations; binomials, nCr.<h2 style="text-align: left;">A start</h2><ul style="text-align: left;"><li>First thing was to try and see patterns in the combinations of d1+d2 minus those of just d1.</li><li>Dealing with sets of values; sets are unordered, so will at some  time need a function to print them in order to aid pattern finding.</li><li>Combinations can be large - use combinations of ints then later work with any type.</li><li>Initial combinations of 0 to n-1 ints I later found to be more awkward to reason about so changed to work with combinations of 1 to n ints. extending to n+x added ints n+1 to n+x to combinations.</li></ul><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div>#!/bin/env python3</div><div>"""</div><div>Check patterns in extending combinations nCr</div><div>"""</div> <div># %%</div><div>from functools import reduce</div><div>from itertools import combinations</div><div>from pprint import pformat</div><div>from typing import Any, Iterable</div> <div># %%</div><div>def nCr(n, r):</div><div>    return combinations(range(1, n+1), r=r)</div> <div>def pf_set(s: set) -> str:</div><div>    "Format set with sorted elements for printing"</div><div>    return  f"{{{pformat(sorted(s), width=120, compact=True)[1:-1]}}}"</div> <div></div></div><h3 style="text-align: left;">Diffs</h3>In the following cell I create combinations for r = 3, and n in some range n_range in c and print successive combinations, and differences between successive combinations.<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div>r = 3</div><div>n_range = range(3, 7)</div><div>print(f"\n# Investigate combinations nCr for {r=}, and {n_range=}\n")</div> <div># generate some combinations</div><div>c = {n: set(nCr(n, r)) for n in n_range}</div> <div>print("c = {")</div><div>for key, val in c.items():</div><div>    print(f" {key:2}: {pf_set(val)},")</div><div>print("    }")</div> <div>def pp_diffsby(c, n_range, r, delta_n):</div><div>    "Print nCr in c diffs by delta_n"</div><div>    print(f"\nDiffs by {delta_n}")</div><div>    all_n = list(n_range)</div><div>    n1 = all_n[0]</div><div>    print(f"      {n1}C{r} = {pf_set(c[n1])}")</div><div>    for n1, n2 in zip(all_n, all_n[delta_n:]):</div><div>        print(f"{n2}C{r} - {n1}C{r} = {pf_set(c[n2] - c[n1])}")</div> <div>pp_diffsby(c, n_range, r, 1)</div><div>pp_diffsby(c, n_range, r, 2)</div> </div><h4 style="text-align: left;">Cell output:</h4><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># Investigate combinations nCr for r=3, and n_range=range(3, 7)</div> <div>c = {</div><div>  3: {(1, 2, 3)},</div><div>  4: {(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)},</div><div>  5: {(1, 2, 3), (1, 2, 4), (1, 2, 5), (1, 3, 4), (1, 3, 5), (1, 4, 5), (2, 3, 4), (2, 3, 5), (2, 4, 5), (3, 4, 5)},</div><div>  6: {(1, 2, 3), (1, 2, 4), (1, 2, 5), (1, 2, 6), (1, 3, 4), (1, 3, 5), (1, 3, 6), (1, 4, 5), (1, 4, 6), (1, 5, 6),</div><div> (2, 3, 4), (2, 3, 5), (2, 3, 6), (2, 4, 5), (2, 4, 6), (2, 5, 6), (3, 4, 5), (3, 4, 6), (3, 5, 6), (4, 5, 6)},</div><div>    }</div> <div>Diffs by 1</div><div>      3C3 = {(1, 2, 3)}</div><div>4C3 - 3C3 = {(1, 2, 4), (1, 3, 4), (2, 3, 4)}</div><div>5C3 - 4C3 = {(1, 2, 5), (1, 3, 5), (1, 4, 5), (2, 3, 5), (2, 4, 5), (3, 4, 5)}</div><div>6C3 - 5C3 = {(1, 2, 6), (1, 3, 6), (1, 4, 6), (1, 5, 6), (2, 3, 6), (2, 4, 6), (2, 5, 6), (3, 4, 6), (3, 5, 6), (4, 5, 6)}</div> <div>Diffs by 2</div><div>      3C3 = {(1, 2, 3)}</div><div>5C3 - 3C3 = {(1, 2, 4), (1, 2, 5), (1, 3, 4), (1, 3, 5), (1, 4, 5), (2, 3, 4), (2, 3, 5), (2, 4, 5), (3, 4, 5)}</div><div>6C3 - 4C3 = {(1, 2, 5), (1, 2, 6), (1, 3, 5), (1, 3, 6), (1, 4, 5), (1, 4, 6), (1, 5, 6), (2, 3, 5), (2, 3, 6), (2, 4, 5),</div><div> (2, 4, 6), (2, 5, 6), (3, 4, 5), (3, 4, 6), (3, 5, 6), (4, 5, 6)}</div> </div><h2 style="text-align: left;">Patterns</h2>Looking at diffs 4C3 - 3C3 each tuple is like they took 3C2 = {(1,2), (1,3), (2,3)} and tagged the extra 4 on to every inner tuple. Lets call this modification extending,5C3 - 4C3 seems to follow the same pattern.<h3 style="text-align: left;">Function extend</h3><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div>def extend(s: set[tuple[Any]], value: Any) -> set[tuple[Any]]:</div><div>    """</div><div>    Returns set of tuples of s with each tuple extended by value</div><div>    """</div><div>    return set((*tpl, value) for tpl in s)</div> <div>s = {(0, 1), (1, 2), (0, 2)}</div><div>print(f"{s = }; {extend(s, 3) = }")</div><div>assert extend(s, 3) == {(0, 1, 3), (1, 2, 3), (0, 2, 3)}</div> <div></div></div><h3 style="text-align: left;">Rename nCr to bino and check extend works</h3>nCr  was originally working with 0..n-1 and bino was 1..n. Now they both do<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div># binomial combinations of ints 1..</div> <div>def bino(n: int, r: int) -> set[tuple[int]]:</div><div>    """</div><div>    All combinations of 1..n ints taken r at a time</div> <div>    bino(4, 3) == {(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)}</div><div>    """</div><div>    return set(combinations(range(1, n+1), r))</div> <div>print(f"{(bino(4, 3) == (bino(3, 3) | extend(bino(3, 2), value=4)))  =  }")</div> </div><h4 style="text-align: left;">Cell output:</h4><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div style="line-height: 19px;"><div>(bino(4, 3) == (bino(3, 3) | extend(bino(3, 2), value=4)))  =  True</div><div> </div></div></div><h2 style="text-align: left;">Pascal</h2><div>After finding that pattern I went searching for it using Gemini AI. My question was:</div><div style="text-align: left;"><blockquote>show that comb(n+1, r) = comb(n, r) + (n+1)* comb(n, r-1)</blockquote></div><div>The answer said I got my text prompt wrong and mentioned <a href="https://en.wikipedia.org/wiki/Pascal%27s_rule" rel="nofollow" target="_blank">Pascals Rule</a>.</div><div>I scanned the page, as I no time for how things were expressed but it seemed reasonable that I had the algorithm right, and that there were relations of some kind for bigger differences in n.</div><div> </div><h3 style="text-align: left;">Pascals rule checker</h3><div>I wrote a function to do the check then exercised it  (a few times, now deleted).</div><div> </div><div><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div><div>def pascals_rule(n: int, r: int) -> bool:</div><div>    "check C(n+1, r) == C(n, r) | extend(C(n, r-1), n + 1)"</div><div>    return bino(n + 1, r) == bino(n, r) | extend(bino(n, r - 1), n + 1)</div> <div>assert pascals_rule(6, 3)</div></div></div><div> </div><h2 style="text-align: left;">Diff by 1 extra item "done", attempting diff by 2.</h2><div>Looking back at the diffs by 2 table and looking for patterns I thought I might need different types of extension functions modifying the tuples within sets in different ways - it seemed "mathematical" so...</div><div> </div><div><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div># Some functions that may be needed</div> <div>def extend_mul(s: set[tuple[Any]], e: Iterable) -> set[tuple[Any]]:</div><div>    """</div><div>    set where each tuple of s is extended in turn, by every item in e</div> <div>    s = {(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)}</div><div>    extend_mul(s, (5, 6)) == {(1, 2, 3, 5), (1, 2, 3, 6),</div><div>                              (1, 2, 4, 5), (1, 2, 4, 6),</div><div>                              (1, 3, 4, 5), (1, 3, 4, 6),</div><div>                              (2, 3, 4, 5), (2, 3, 4, 6)}</div><div>    """</div><div>    return {(*t, item) for t in s for item in e}</div> <div>def extend_muli(s: set[tuple[Any]], e: Iterable) -> set[tuple[Any]]:</div><div>    """</div><div>    set where each tuple of s is extended in turn, by every *item in e</div> <div>    s = {(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)}</div><div>    extend_muli(s, ((5,), (6,))) == {(1, 2, 3, 5), (1, 2, 3, 6),</div><div>                              (1, 2, 4, 5), (1, 2, 4, 6),</div><div>                              (1, 3, 4, 5), (1, 3, 4, 6),</div><div>                              (2, 3, 4, 5), (2, 3, 4, 6)}</div><div>    """</div><div>    return {(*t, *item) for t in s for item in e}</div> <div>def extend_add(s: set[tuple[Any]], e: Iterable) -> set[tuple[Any]]:</div><div>    """</div><div>    set where each tuple of s is extended once, all  items of *e</div> <div>    s = {(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)}</div><div>    extend_add(s, (5, 6)) == {(1, 2, 3, 5, 6), (1, 2, 4, 5, 6),</div><div>                              (1, 3, 4, 5, 6), (2, 3, 4, 5, 6)}</div><div>    """</div><div>    return {(*t, *e) for t in s}</div> </div></div><div> </div><h2 style="text-align: left;">Diff by 2 pattern finding</h2>It was incremental - find a pattern in 3C?, subtract it from 5C3, Find a pattern in 3C? that covers part of the remainder; repeat. (Where ? <=3).I also shortened function pf_set to pf so it would take less space when printing formatted expressions in f-strings<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div>print(f"To simplify formatted printing of sets: {(pf:=pf_set) = }\n")</div> <div>print("# Looking again at the diffs by 2 i.e. `5C3 - 3C3`")</div><div>n, r, x = 3, 3, 2</div> <div>print(f"5C3 - 3C3 = {pf(bino(5, 3) - bino(3, 3))}")</div><div>print(f"\n  There's {pf(bino(3, 3-1)) = } in there with each tuple extended by 4 and by 5 ")</div><div>print(f"\n  {pf(extend_mul(bino(3, 3-1), (3+1, 3+2))) = }")</div><div>print(f"\n  Whats left: {pf(tmp1 := (bino(5, 3) - bino(3, 3) - extend_mul(bino(3, 3-1), (3+1, 3+2)))) = }")</div><div>print(f"\n    Now {pf(bino(3, 3-2)) = }")</div><div>print(f"\n    So {pf(tmp2 := (extend_add(bino(3, 3-2), (3+1, 3+2)))) = }")</div><div>print(f"\n  Finally: {pf(tmp1 - tmp2) = }")</div></div><h4 style="text-align: left;">Cell output:</h4><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div>To simplify formatted printing of sets: (pf:=pf_set) = <function pf_set at 0x7f1bd45365c0></div> <div># Looking again at the diffs by 2 i.e. `5C3 - 3C3`</div><div>5C3 - 3C3 = {(1, 2, 4), (1, 2, 5), (1, 3, 4), (1, 3, 5), (1, 4, 5), (2, 3, 4), (2, 3, 5), (2, 4, 5), (3, 4, 5)}</div> <div>  There's pf(bino(3, 3-1)) = '{(1, 2), (1, 3), (2, 3)}' in there with each tuple extended by 4 and by 5 </div> <div>  pf(extend_mul(bino(3, 3-1), (3+1, 3+2))) = '{(1, 2, 4), (1, 2, 5), (1, 3, 4), (1, 3, 5), (2, 3, 4), (2, 3, 5)}'</div> <div>  Whats left: pf(tmp1 := (bino(5, 3) - bino(3, 3) - extend_mul(bino(3, 3-1), (3+1, 3+2)))) = '{(1, 4, 5), (2, 4, 5), (3, 4, 5)}'</div> <div>    Now pf(bino(3, 3-2)) = '{(1,), (2,), (3,)}'</div> <div>    So pf(tmp2 := (extend_add(bino(3, 3-2), (3+1, 3+2)))) = '{(1, 4, 5), (2, 4, 5), (3, 4, 5)}'</div> <div>  Finally: pf(tmp1 - tmp2) = '{}'</div></div><h3 style="text-align: left;">Behold Diff by 2</h3><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div><div>n, r, x = None, None, None</div> <div>print(f"\n# lets set some variables and use those for the diffs by 2")</div><div>print(f"  {(n:=3), (r:=3) = }")</div><div>print(f"  {len(bino(n+2, r)) = }")</div><div>print(f"  {bino(n+2, r) == (bino(n, r) | extend_mul(bino(n, r-1), (n+1, n+2)) | extend_add(bino(n, r-2), (n+1, n+2))) = }")</div></div><h4 style="text-align: left;">Cell output:</h4><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># lets set some variables and use those for the diffs by 2</div><div>  (n:=3), (r:=3) = (3, 3)</div><div>  len(bino(n+2, r)) = 10</div><div>  bino(n+2, r) == (bino(n, r) | extend_mul(bino(n, r-1), (n+1, n+2)) | extend_add(bino(n, r-2), (n+1, n+2))) = True</div></div><h2 style="text-align: left;">Pascals rules for increasing diffs</h2>I followed the same method for diffs of three and ended up with these three functions:<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div># By similar observation and much checking:</div><div>def pascal_rule_1(n, r) -> bool:</div><div>    """</div><div>    Checks bino(n+1, r) == (bino(n, r)</div><div>                            | extend_add(bino(n, r-1), (n+1,)))</div><div>    """</div><div>    return bino(n+1, r) == (bino(n, r)</div><div>                            | extend_add(bino(n, r-1), (n+1,)))</div> <div>def pascal_rule_2(n, r) -> bool:</div><div>    """</div><div>    Checks bino(n+2, r) == (bino(n, r)</div><div>                            | extend_mul(bino(n, r-1), (n+1, n+2))</div><div>                            | extend_add(bino(n, r-2), (n+1, n+2)))</div><div>    """</div><div>    return bino(n+2, r) == (bino(n, r)</div><div>                            | extend_mul(bino(n, r-1), (n+1, n+2))</div><div>                            | extend_add(bino(n, r-2), (n+1, n+2)))</div> <div>def pascal_rule_3(n, r) -> bool:</div><div>    """</div><div>    Checks bino(n+3, r) == (bino(n, r)</div><div>                            | extend_muli(bino(n, r-1), tuple(combinations((n+1, n+2, n+3), 1)))</div><div>                            | extend_muli(bino(n, r-2), tuple(combinations((n+1, n+2, n+3), 2)))</div><div>                            | extend_muli(bino(n, r-3), tuple(combinations((n+1, n+2, n+3), 3)))</div><div>                            )</div><div>    """</div><div>    extra_n = tuple(range(n+1, n+4))  # n..n+3 inclusive</div><div>    return bino(n+3, r) == (bino(n, r)</div><div>                            | extend_muli(bino(n, r-1), tuple(combinations(extra_n, 1)))</div><div>                            | extend_muli(bino(n, r-2), tuple(combinations(extra_n, 2)))</div><div>                            | extend_muli(bino(n, r-3), tuple(combinations(extra_n, 3)))</div><div>                            )</div> <div># %%</div><div># Simple Checks</div><div>assert pascal_rule_1(7, 4)</div><div>assert pascal_rule_2(9, 4)</div><div>assert pascal_rule_3(11, 4)</div> </div><h3 style="text-align: left;">Generalised Pascals rule</h3>What can I say, I looked for patterns in the pascals rule functions for discrete diffs and tried to find patterns. I looked deeper into identities between the extend functions.I finally found the following function that passed my tests, (many not shown). <div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div><div># from pascal_rule_3 to pascal_rule_x</div> <div>def pascal_rule_x(n, r, x) -> bool:</div><div>    """</div><div>    Checks bino(n+x, r) == union_reduce(<extend_muli selections of bino(n, 0 < r < r)> )</div> <div>    ie if already used bino(n, r) and along comes x more for n then it shows how to</div><div>    calculate bino(n+x, r) without the need for storing bino(n, r)</div> <div>    """</div><div>    extra_n = tuple(range(n+1, n+x+1))  # n..n+x inclusive</div><div>    n_r_terms = (extend_muli((bino(n, r-i) if r-i > 0 else {()}),   # extend this</div><div>                             tuple(combinations(extra_n, i)))       # by this</div><div>                for i in range(min(x, r) + 1))</div><div>    reduction = reduce(set.union, n_r_terms, set())</div><div>    return bino(n+x, r) == reduction</div> <div>assert pascal_rule_x(11, 4, 3)  # n, r, x</div><div>assert pascal_rule_x(11, 5, 4)</div><div>assert pascal_rule_x(3, 2, 3)</div></div>I don't like the if r-i > 0 else {()} bit as it doesn't seem elegant. There is probably some identity to be found that would make it disappear but, you know.<h1 style="text-align: left;">Back to the original problem</h1>If comb(d1, r) is processed and then we find an extra d2 items, then we want to process extra_comb(d1, r, d2) where extra_comb does not include or save comb of d1.We just need to exclude the nCr term in reduction of function pascal_rule_x.Eventually I arrive at<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div>first = list(combinations('abcd', 3))</div><div>first</div><div># %%</div><div>all = list(combinations('abcdef', 3))</div><div># %%</div><div>extra = sorted(set(all) - set(first))</div><div>extra</div><div># %%</div> <div>def extra_combs(orig='abcd', r=3, delta='ef'):</div><div>    C = combinations  # Less typing</div><div>    extra_n = tuple(delta)  # n..n+x inclusive</div><div>    n = tuple(orig)</div><div>    n_r_terms = (extend_muli((C(n, r-i) if r-i > 0 else {()}),   # extend this</div><div>                             tuple(C(extra_n, i)))               # by this</div><div>                for i in range(1, min(len(extra_n), r) + 1))     # miss C(n, r)</div><div>    reduction = reduce(set.union, n_r_terms, set())</div><div>    # set(C(n+extra_n, r)) - reduction == set(C(n, r))</div><div>    return reduction</div> <div>n, r, delta = 'abcd', 3, 'efg'</div><div>assert set(combinations(n+delta, r)) \</div><div>         == set(combinations(n, r)).union(extra_combs(n, r, delta))</div></div><h2 style="text-align: left;">Tests</h2><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, "Courier New", monospace; font-size: 14px; line-height: 19px; white-space: pre;"><div># %%</div> <div># Test comb(n+x, r) == comb(n, r) | extra_combs(n, r, x)</div> <div>n, r, delta_ = 'abcdefg', 3, 'hijkl'</div> <div>for r in range(len(n)):</div><div>    for delta in (delta_[:i] for i in range(len(delta_))):</div><div>        combnx = set(combinations(n+delta, r))</div><div>        combn = set(combinations(n, r))</div><div>        extra = extra_combs(n, r, delta)</div><div>        assert combnx == (combn | extra), f"Whoops! For  {(n, r, delta) = }"</div><div>        # checks that extra does not generate any of combn</div><div>        assert not extra.intersection(combn), f"Whoops! For  {(n, r, delta) = }"</div> </div> END. Sun, 23 Mar 2025 08:54:00 UTCIntsets by AIhttp://paddy3118.blogspot.com/feeds/670262973629941969/comments/default<div _ngcontent-ng-c2089202004="" class="markdown markdown-main-panel stronger" dir="ltr" id="model-response-message-contentr_ef47054a1625a71d" style="--animation-duration: 600ms; --fade-animation-function: linear;"> <div class="attachment-container generated-images"><div _ngcontent-ng-c458529192="" class="loader animate"></div><div _ngcontent-ng-c2713074886="" class="image-container replace-fife-images-at-export ng-star-inserted" style="text-align: center;"><div _ngcontent-ng-c2713074886="" class="overlay-container ng-star-inserted"><button _ngcontent-ng-c2713074886="" class="image-button ng-star-inserted"><img _ngcontent-ng-c2713074886="" alt="Image of " class="image animate loaded" 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" /></button></div></div></div> </div> I will be using sets a lot in a future algorithm and wanted to find out about the speed of operation of different set implementations.I will be dealing with different sets of strings from a total of around 200_000, (but the strings being added throughout the calculations). Python sets can do that directly, but I can create an integer index of all the strings and then do calculations on sets of integers.Sets of integers can be translated to ints with bits set to indicate which ints were in the set. Numpy can do this but needs either all the strings up-front, or the maximum integer up front.  Python integers are variable length, as the algorithm adds more strings, they can be looked up and either their index returned or an incrementing index assigned and returned if the string is new. if you want to set the seven thousandth and one bit of a Python int then the int will extend as necessary to allow this.<h4 style="text-align: left;">Code by AI </h4>I decided to talk to Gemini AI to create all code and after a couple of hours of asking, refining and my testing I got my code. (Mine as I expended effort in its creation and debugging, even if it was by questioning Gemini).<h4 style="text-align: left;">Result!</h4>I found that intsets can be hundreds of times faster than the same operation with the same values but with sets. (Frozensets in this particular case ase I cache a conversion function).<div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>max_int: 262144, set_count: 5, repeat_count: 1</div><div>Average time for intset operations: 0.001064 seconds</div><div>Average time for set operations: 0.921396 seconds</div><div>Intset operations are approximately 866.31 times faster than set operations.</div><div> </div></div><h4 style="text-align: left;"> Code</h4><div style="background-color: #1f1f1f; color: #cccccc; font-family: Consolas, 'Courier New', monospace; font-size: 14px; font-weight: normal; line-height: 19px; white-space: pre;"><div>"""</div><div>This module provides functions for generating random sets, performing set operations,</div><div>and timing the performance of set operations using both standard Python sets and</div><div>integer-based sets (intsets). It includes caching for intset conversions and calculates</div><div>the speedup of intset operations over standard set operations.</div><div>"""</div> <div>import random</div><div>import time</div><div>import itertools</div><div>from functools import lru_cache</div><div>from typing import FrozenSet, Tuple, List</div> <div>def generate_random_sets_with_sample(max_int: int, set_count: int) -> List[FrozenSet[int]]:</div><div>    """</div><div>    Generates a list of random frozensets of integers using random.sample.</div> <div>    Args:</div><div>        max_int: The maximum integer value in the sets.</div><div>        set_count: The number of random sets to generate.</div> <div>    Returns:</div><div>        A list of random frozensets of integers.</div><div>    """</div><div>    all_ints = frozenset(range(max_int + 1))</div><div>    random_sets = []</div> <div>    for _ in range(set_count):</div><div>        population = range(max_int + 1)</div><div>        sample_size = random.randint(0, max_int + 1)</div><div>        current_set = frozenset(random.sample(population, sample_size))</div><div>        random_sets.append(current_set)</div> <div>    random_sets.append(all_ints)  # Append all_ints to random_sets</div><div>    return random_sets</div> <div>def intset_difference(intset1: int, intset2: int) -> int:</div><div>    """Calculates the difference of two intsets."""</div><div>    return intset1 & ~intset2</div> <div>def intset_intersection(intset1: int, intset2: int) -> int:</div><div>    """Calculates the intersection of two intsets."""</div><div>    return intset1 & intset2</div> <div>def intset_symmetric_difference(intset1: int, intset2: int) -> int:</div><div>    """Calculates the symmetric difference of two intsets."""</div><div>    return intset1 ^ intset2</div> <div>def intset_union(intset1: int, intset2: int) -> int:</div><div>    """Calculates the union of two intsets."""</div><div>    return intset1 | intset2</div> <div>def set_difference(set1: FrozenSet[int], set2: FrozenSet[int]) -> FrozenSet[int]:</div><div>    """Calculates the difference of two frozensets."""</div><div>    return set1.difference(set2)</div> <div>def set_intersection(set1: FrozenSet[int], set2: FrozenSet[int]) -> FrozenSet[int]:</div><div>    """Calculates the intersection of two frozensets."""</div><div>    return set1.intersection(set2)</div> <div>def set_symmetric_difference(set1: FrozenSet[int], set2: FrozenSet[int]) -> FrozenSet[int]:</div><div>    """Calculates the symmetric difference of two frozensets."""</div><div>    return set1.symmetric_difference(set2)</div> <div>def set_union(set1: FrozenSet[int], set2: FrozenSet[int]) -> FrozenSet[int]:</div><div>    """Calculates the union of two frozensets."""</div><div>    return set1.union(set2)</div> <div>@lru_cache(maxsize=None)</div><div>def set_to_intset(input_set: FrozenSet[int]) -> int:</div><div>    """</div><div>    Converts a frozenset of integers to an intset, with caching.</div> <div>    Args:</div><div>        input_set: The frozenset of integers.</div> <div>    Returns:</div><div>        The intset representation of the frozenset.</div><div>    """</div><div>    result = 0</div><div>    for num in input_set:</div><div>        result |= (1 << num)</div><div>    return result</div> <div>def time_set_operations(random_sets: List[FrozenSet[int]], repeat_count: int = 1) -> Tuple[float, float]:</div><div>    """</div><div>    Times set and intset operations multiple times and returns average timings.</div> <div>    Args:</div><div>        random_sets: A list of frozensets.</div><div>        repeat_count: The number of times to repeat the timing.</div> <div>    Returns:</div><div>        A tuple containing the average intset operation time and the average set operation time.</div><div>    """</div><div>    total_intset_time = 0</div><div>    total_set_time = 0</div> <div>    for _ in range(repeat_count):</div><div>        intset_time = 0</div><div>        set_time = 0</div> <div>        for set1, set2 in itertools.permutations(random_sets, 2):</div><div>            # Time intset operations</div><div>            intset1 = set_to_intset(set1)</div><div>            intset2 = set_to_intset(set2)</div> <div>            start_intset = time.time()</div><div>            intset_difference(intset1, intset2)</div><div>            intset_intersection(intset1, intset2)</div><div>            intset_symmetric_difference(intset1, intset2)</div><div>            intset_union(intset1, intset2)</div><div>            end_intset = time.time()</div><div>            intset_time += end_intset - start_intset</div> <div>            # Time set operations</div><div>            start_set = time.time()</div><div>            set_difference(set1, set2)</div><div>            set_intersection(set1, set2)</div><div>            set_symmetric_difference(set1, set2)</div><div>            set_union(set1, set2)</div><div>            end_set = time.time()</div><div>            set_time += end_set - start_set</div> <div>        total_intset_time += intset_time</div><div>        total_set_time += set_time</div> <div>    avg_intset_time = total_intset_time / repeat_count</div><div>    avg_set_time = total_set_time / repeat_count</div> <div>    return avg_intset_time, avg_set_time</div> <div># Example Usage:</div><div>max_int = 2**18</div><div>set_count = 5</div><div>repeat_count = 1</div> <div>random_sets = generate_random_sets_with_sample(max_int, set_count)</div> <div>avg_intset_time, avg_set_time = time_set_operations(random_sets, repeat_count)</div> <div>print(f"max_int: {max_int}, set_count: {set_count}, repeat_count: {repeat_count}")</div><div>print(f"Average time for intset operations: {avg_intset_time:.6f} seconds")</div><div>print(f"Average time for set operations: {avg_set_time:.6f} seconds")</div> <div>if avg_intset_time > 0:</div><div>    speedup = avg_set_time / avg_intset_time</div><div>    print(f"Intset operations are approximately {speedup:.2f} times faster than set operations.")</div><div>else:</div><div>    print("Set operations time was zero. Unable to calculate speedup.")</div><div>    </div></div> <h4 style="text-align: left;">My Gemini  chat</h4>1.  python function set_to_intset that given a set containing integers returns an     int with just those binary bits set to one; and a reverse function called     intset_to_set that given an int returns a set of ints where the ints are the     positions of all the bits that are one in the intset argument. 2.  methods between two python sets: difference, intersection,     symmetric_difference and union can be achieved between two intsets using     logical operators and logical functions. What are they? 3.  add a functionwith arguments max_int, set_count, and repeats; generates     set_count random sets of the ints 0 to max_int and the set of all ints from     zero to max_int. 4.  use random.sample with a randomised sample count 5.  population should be the range 6.  append all_ints to random_sets then test the stated set functions on all     permutations of two of the random sets. accumulate the execution time of the     set functions 7.  remove list from variable permutations then do not use an intermediate     variable permutations, just iterate on its expression. 8.  Do similar timings for intset transformations of all random_sets 9.  No, keep the method timings as before. 10. keep two time accumulators, one for timing the runs of the set functions; do     not time the transformations between set and intset; use the other timer for     timing the intset functions. return two two times 11. do not time the conversions. time the set operations too 12. time_set_operations should take a repeat count and repeat the for loop that     many times then return the avrage timings for each of sets, intsets 13. do not print the sets. print max_int, set_count, and repeat_count 14. show how much faster intset is 15. speedup calculation is wrong 16. speedup of intset is intset time / set time 17. no it is with respect to set time so divide by set time 18. that is not what I typed! 19. I was wrong about the speedum calculation 20. use it 21. inline the set andintset calculations when timing instead of calling out to     the set_* and intset_+ functions, (but keep their definitions) 22. use max_int = 3**18 and set_count = 10 23. use max_int = 2**18; set_count = 5; repeat_count = 1 24. use lru_cached versions of function set_to_intset 25. generate random_sets as frozensets directly 26. add function type_hints 27. only have type hinting on function arguments and return values 28. add docstrings to all functions and add a module docstringEnd.Wed, 05 Mar 2025 22:14:00 UTC