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Starting on the matrix #337

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merged 4 commits into from
Nov 13, 2024
Merged

Starting on the matrix #337

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b26eb24
Starting on the matrix
hosseinmoein Nov 7, 2024
02b2f5a
Basics of matrix object is done
hosseinmoein Nov 9, 2024
2b0fd33
Stable matrix version
hosseinmoein Nov 11, 2024
266e347
Added arithmetic operators and tests
hosseinmoein Nov 12, 2024
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2 changes: 1 addition & 1 deletion docs/HTML/DataFrame.html
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Original file line number Diff line number Diff line change
Expand Up @@ -622,7 +622,7 @@ <H2 ID="2"><font color="blue">API Reference with code samples <font size="+4">&#
</tr>

<tr class="item" onmouseover="this.style.backgroundColor='#ffff66';" onmouseout="this.style.backgroundColor='#d4e3e5';">
<td title="These are other functionalities of DataFrame" style="text-align:center;background-color:LightGrey;color:DarkBlue">Gears &nbsp;&nbsp; <font size="+3">&#x2699;</font></td>
<td title="These are other functionalities of DataFrame" style="text-align:center;background-color:LightGrey;color:DarkBlue">Gears &amp; Stuff &nbsp;&nbsp; <font size="+3">&#x2699;</font></td>
</tr>

<tr class="item" onmouseover="this.style.backgroundColor='#ffff66';" onmouseout="this.style.backgroundColor='#d4e3e5';">
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2 changes: 1 addition & 1 deletion docs/HTML/self_contained.html
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Expand Up @@ -51,7 +51,7 @@
It also has some disadvantages:
<UL>
<LI>There might be functionalities that are hard/time-consuming to implement that are already there</LI>
<LI>If you find a battle-test library, the debugging is already done for you</LI>
<LI>If you find a battle-tested library, the debugging is already done for you</LI>
<LI>There might be industry-wide standards/trends that you want to follow by using a reputed library</LI>
</UL>
<BR>
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62 changes: 31 additions & 31 deletions include/DataFrame/DataFrameStatsVisitors.h
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Expand Up @@ -6382,16 +6382,12 @@ struct LinearFitVisitor {
const H &x_begin, const H &x_end,
const H &y_begin, const H &y_end) {

const size_type col_s = std::distance(x_begin, x_end);
const size_type col_s =
std::min(std::distance(x_begin, x_end),
std::distance(y_begin, y_end));
const auto thread_level = (col_s < ThreadPool::MUL_THR_THHOLD)
? 0L : ThreadGranularity::get_thread_level();

#ifdef HMDF_SANITY_EXCEPTIONS
if (col_s != size_type(std::distance(y_begin, y_end)))
throw DataFrameError("LinearFitVisitor: two columns must be "
"of equal sizes");
#endif // HMDF_SANITY_EXCEPTIONS

value_type sum_x { 0 }; // Sum of all observed x
value_type sum_y { 0 }; // Sum of all observed y
value_type sum_x2 { 0 }; // Sum of all observed x squared
Expand Down Expand Up @@ -7543,30 +7539,32 @@ is_normal(const V &column, double epsl, bool check_for_standard) {
svisit.post();

const value_type mean = static_cast<value_type>(svisit.get_mean());
const value_type std = static_cast<value_type>(svisit.get_std());
const value_type high_band_1 = static_cast<value_type>(mean + std);
const value_type low_band_1 = static_cast<value_type>(mean - std);
const value_type stdev = static_cast<value_type>(svisit.get_std());
const value_type high_band_1 = static_cast<value_type>(mean + stdev);
const value_type low_band_1 = static_cast<value_type>(mean - stdev);
double count_1 = 0.0;
const value_type high_band_2 =
static_cast<value_type>(mean + std * 2.0);
const value_type low_band_2 = static_cast<value_type>(mean - std * 2.0);
static_cast<value_type>(mean + stdev * 2.0);
const value_type low_band_2 =
static_cast<value_type>(mean - stdev * 2.0);
double count_2 = 0.0;
const value_type high_band_3 =
static_cast<value_type>(mean + std * 3.0);
const value_type low_band_3 = static_cast<value_type>(mean - std * 3.0);
static_cast<value_type>(mean + stdev * 3.0);
const value_type low_band_3 =
static_cast<value_type>(mean - stdev * 3.0);
double count_3 = 0.0;

for (auto citer : column) [[likely]] {
if (citer >= low_band_1 && citer < high_band_1) {
for (const auto &val : column) [[likely]] {
if (val >= low_band_1 && val < high_band_1) {
count_3 += 1;
count_2 += 1;
count_1 += 1;
}
else if (citer >= low_band_2 && citer < high_band_2) {
else if (val >= low_band_2 && val < high_band_2) {
count_3 += 1;
count_2 += 1;
}
else if (citer >= low_band_3 && citer < high_band_3) {
else if (val >= low_band_3 && val < high_band_3) {
count_3 += 1;
}
}
Expand All @@ -7578,7 +7576,7 @@ is_normal(const V &column, double epsl, bool check_for_standard) {
std::fabs((count_3 / col_s) - 0.997) <= epsl) {
if (check_for_standard)
return (std::fabs(mean - 0) <= epsl &&
std::fabs(std - 1.0) <= epsl);
std::fabs(stdev - 1.0) <= epsl);
return (true);
}
return (false);
Expand All @@ -7597,28 +7595,30 @@ is_lognormal(const V &column, double epsl) {
StatsVisitor<value_type, int> log_visit;

svisit.pre();
for (auto citer : column) [[likely]] {
svisit(dummy_idx, static_cast<value_type>(std::log(citer)));
log_visit(dummy_idx, citer);
for (auto val : column) [[likely]] {
svisit(dummy_idx, static_cast<value_type>(std::log(val)));
log_visit(dummy_idx, val);
}
svisit.post();

const value_type mean = static_cast<value_type>(svisit.get_mean());
const value_type std = static_cast<value_type>(svisit.get_std());
const value_type high_band_1 = static_cast<value_type>(mean + std);
const value_type low_band_1 = static_cast<value_type>(mean - std);
const value_type stdev = static_cast<value_type>(svisit.get_std());
const value_type high_band_1 = static_cast<value_type>(mean + stdev);
const value_type low_band_1 = static_cast<value_type>(mean - stdev);
double count_1 = 0.0;
const value_type high_band_2 =
static_cast<value_type>(mean + std * 2.0);
const value_type low_band_2 = static_cast<value_type>(mean - std * 2.0);
static_cast<value_type>(mean + stdev * 2.0);
const value_type low_band_2 =
static_cast<value_type>(mean - stdev * 2.0);
double count_2 = 0.0;
const value_type high_band_3 =
static_cast<value_type>(mean + std * 3.0);
const value_type low_band_3 = static_cast<value_type>(mean - std * 3.0);
static_cast<value_type>(mean + stdev * 3.0);
const value_type low_band_3 =
static_cast<value_type>(mean - stdev * 3.0);
double count_3 = 0.0;

for (auto citer : column) [[likely]] {
const auto log_val = std::log(citer);
for (const auto &val : column) [[likely]] {
const auto log_val = std::log(val);

if (log_val >= low_band_1 && log_val < high_band_1) {
count_3 += 1;
Expand Down
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